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News About Tech, Money and InnovationSun, 02 Aug 2015 23:11:56 +0000en-UShourly1http://wordpress.org/?v=4.2.3Copyright 2015, VentureBeatData scientists to CEOs: You can’t handle the truthhttp://venturebeat.com/2015/08/01/data-scientists-to-ceos-you-cant-handle-the-truth/
http://venturebeat.com/2015/08/01/data-scientists-to-ceos-you-cant-handle-the-truth/#commentsSat, 01 Aug 2015 16:00:06 +0000http://venturebeat.com/?p=1774718GUEST: Too many big data initiatives fail because companies, top to bottom, aren’t committed to the truth in analytics. Let me explain. In January 2015, the Economist Intelligence Unit (EIU) and Teradata (full disclosure: also my employer) released the results of a major study aimed at identifying how businesses that are successful at being data-driven differ from those […]
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GUEST:

Too many big data initiatives fail because companies, top to bottom, aren’t committed to the truth in analytics. Let me explain.

In January 2015, the Economist Intelligence Unit (EIU) and Teradata (full disclosure: also my employer) released the results of a major study aimed at identifying how businesses that are successful at being data-driven differ from those that are not.

Among its many findings, there were some particularly troubling, “code red” results that revealed CEOs seem to have a rosier view of a company’s analytics efforts than directors, managers, analysts, and data scientists. For example, EIU found that CEOs are more likely to think that employees extract relevant insights from the company’s data – 38 percent of them hold this belief, as compared to 24 percent of all respondents and only 19 percent of senior vice presidents, vice presidents, and directors. Similarly, 43 percent of CEOs think relevant data are captured and made available in real time, compared to 29 percent of all respondents.

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So why is there such a disconnect? It turns out the answer is much more human than the size of a company’s data coffers, or the technology stockpiled to analyze it. Big data initiatives fall down at the feet of biases, bad assumptions, and the failure, or fear, of letting the data speak for itself. As insights make their way up the corporate ladder, from the data scientist to the CEO, the truth in analytics is lost along the way. And this leads to a cumulative effect of unintended consequences.

Communicate the Known-Unknowns to Your CEO

Take the idea of known risks, for example. In analytics, you always have to make some assumptions because the data hardly ever paints a complete picture. So, you have to identify and rank those risks to understand what might happen when assumptions go wrong. In some cases, the risks aren’t tied to big consequences. But, in other cases, it can be devastating.

Look at the stock market crash of 2008. A whole host of people made a simple and logical assumption that home prices would only go up. But most analysts didn’t experiment enough with what would happen if prices actually fell. Well, now we know what would happen. It was almost a global calamity. The people investing in the pre-housing crisis bubble were working on an assumption that was very flawed on many levels. And very few people considered, or realized, the risk until it was too late.

The same thing happens, at generally smaller scales, in businesses. The CEO doesn’t have a clear view of risk. It is up to the data scientists, business analysts and their managers to make the CEO well aware of the risk in assumptions. The CEO needs to understand that there is a critical, level 1 risk in assumptions – in the housing example, if prices were to go down, this whole thing falls apart. Even if that risk is unlikely, at least it is on the table. Many people are uncomfortable discussing such negatives with senior executives and many senior executives don’t like to hear it. But to succeed, everyone must get past that hurdle.

Get Past the Culture of Fear of the Truth

Then there is the fear of the truth, with a bit of cognitive bias thrown in. For example, it is very common that sales people, when asked for their forecast, even armed with data on historical performance and current pipeline, are generally not sure if they are going to hit their number. But, typically, they’ll tell the VP of sales they will hit their forecasts – unless, of course, a miss is very apparent. They share the information they’re expected to share, and withhold any acknowledgement that the numbers are malleable.

The problem arises in the aggregate: The VP gets a rosy picture from five sales people on her team, even though they all have serious doubts, so she puts that assumption in and the data rolls up to the CEO, or CFO. In reality, the metric is underpinned by a huge amount of doubt. The truth is buried under the fear of losing one’s job and the cultural expectation that the goal will be met. Failure is not an option. However, while it is likely several of the sales people will manage to hit their number, the chance that they all will is small. This makes the VPs figures even more unrealistic than the initial estimates.

So what happens? Everyone is shocked when the company misses its forecast. This is an example of where people sugarcoat a little at the low end, and the cumulative effect leads to the business incorrectly forecasting company-wide results.

Don’t Underestimate the Future of the Truth

Another common problem is underestimating, or simply not considering, the confidence level in the analytics results that the CEO is being fed. Maybe we are comfortable with the data and the assumptions, we’ve asked the right questions and we’ve taken the risks into consideration, but we haven’t assessed the confidence level of our predictions. This gets into classic model assessment techniques in analytics. Is the forecast plus-or-minus 1 percent or 20 percent? If it is critical to increase sales by 5 percent and the model predicts 10 percent sales growth within plus-or-minus 5 percent, then we’re probably fine. But if the model predicts 10 percent sales growth plus or minus 15 percent, then we might be closing up shop at the end of the year if we aren’t careful.

So, What Needs to Change?

The culture around how data is viewed and data-driven decisions are made has to change. If a data scientist brings all the assumptions and risks to a boardroom conversation only to get chewed up and spat out, the next time he enters that boardroom, he’ll be sure to hide the negative truths. But, when the culture encourages curiosity and impartial acceptance of the story the data tells, then those who keep the data are free to share what they know and won’t be afraid to point to the data, all the data, and not just the rosy bits. The CEO must take the responsibility to actively ask about risks and foster a culture of transparency. But so too does everyone else. Team members at all levels need to take responsibility for holding to the truth in the data and maintaining complete transparency when communicating up the corporate hierarchy.

Executives have to ask their people to do this due diligence as they pass up the results, and they have to ask the questions back down so it becomes a conversation around data, not simply a one-sided dashboard, or presentation. In some cases, there may not be any material risk, but the fact you intelligently reached that conclusion demonstrates that you have the discipline to make the assessment. As a CEO or senior executive, you can’t assume everyone did a great job of validating all the potential risks and made all the right assumptions. You have to ask for the truth and be willing to handle it.

The fintech explosion has brought profound consumer innovations that increased financial inclusion and made financial management easier for all (and likely, raised the bar on consumer expectations). However, the pace of change is increasing and both banks and fintech innovators need to do even more to ensure they are well-positioned to succeed in the future.

Financial institutions (FI’s) may have just begun to come to grips with online and mobile banking innovations, but recent advances in wearable technology and the Internet of Things (IOT) provides a glimpse of customer engagement expectations in the very near future.

To succeed in this rapidly changing landscape, FIs and fintech innovators need to define how they want to succeed – whether to shape the industry by themselves, create value-added partnerships together, or manage defensively by putting off change. And they need to have a clear strategy to deal with the challenges posed by a massive generational shift in banking expectations.

As a result, the financial services industry is being reshaped by disruptive technology and a partnership between fintech and traditional financial institutions.

As Matt Harris of Bain Capital Ventures has been quoted, “The financial crisis caused the banks to realize they needed to partner with innovators. This has really opened up opportunities.”

In this webinar aimed at disruptive entrepreneurs, our panel of disrupters and financial experts will share valuable insights from the trenches. We’ll look at the needs of consumers and how those are feeding change. We’ll look at the role of financial institutions, and the role of fintech, and where the intersection and partnership opportunities lie. And we’ll look at growth fueled by wearables and the impact of the Internet of Things. If you’re a fintech startup, you won’t want to miss these essential learnings.

]]>0How to build financial apps to meet rapidly-evolving customer expectations (webinar)Got data? How’s that working for you?http://venturebeat.com/2015/07/31/got-data-hows-that-working-for-you/
http://venturebeat.com/2015/07/31/got-data-hows-that-working-for-you/#commentsFri, 31 Jul 2015 15:10:42 +0000http://venturebeat.com/?p=1775405SPONSORED: Mission-motivated. Dashboard-driven. KPIs. Marketers are buzzword masters when discussing marketing strategy with top management. But are they in the driver's seat when it comes to maximizing their data?
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SPONSORED:

This sponsored post is produced in association with Autopilot.

Mission-motivated. Dashboard-driven. KPIs. Marketers are buzzword masters when discussing marketing strategy with top management. But are they in the driver’s seat when it comes to maximizing their data?

There are four kinds of data a marketer might use to create exceptional customer journeys. The challenge is to avoid fragmentation fail and bring these together into one cohesive view using an automated marketing platform that can aggregate the different data streams.

Ever-evolving KPIs

Because data overload is a given today, fragmented or unbundled data makes it much more difficult to form a coherent picture of who your customers are. But in order to create a remarkable customer journey, a company first needs to know specifically which data will be most meaningful — and that means identifying the basket of KPIs that are most important at each stage of the business life cycle. KPIs will vary depending on whether your objectives are to:

Get more active users

Onboard new customers

Reduce churn

Increase customer satisfaction

Fundamentally, KPIs should align perfectly with the key business initiatives a company is driving towards within a given month or year. To determine KPIs, assign them to each business imperative, then ensure the marketing data is acceptable to the business owners or to whoever is managing performance.

The trick to data is having it both dashboard-driven and accessible in real time, so you can gather a team and look at a common set of metrics that allow you to make decisions much more rapidly. A dashboard displaying the most important data efficiently and easily to senior decision makers, coupled with real-time delivery, is key to making impactful decisions or pivots. Companies lacking this KPI data at their fingertips will struggle; companies that have access to it get ahead.

Yet how a company uses its KPIs doesn’t need to be complex. Consider a CrossFit gym, which uses Autopilot to track the rate at which members check into the gym. Lax members are encouraged to frequent the gym; regular users, to refer a friend.

Don’t step on the gas without a GPS

Marketers who try to jump on the data bandwagon without best practices in place are essentially “driving without a license”. To become data-driven, a marketer must:

For example, using a platform such as Salesforce or GoodData means that every morning when senior management clicks their Inbox, there’s a report showing key metrics. These enable them to track key goals for the month and the quarter, so team members know what’s working or not working for that day and week.

In the data driver’s seat: Cambium Networks

Cambium Networks, whose mission is global connectivity, exemplifies how to get the most out of data-driven marketing automation. Their objectives are to grow leads from activating WiFi networks around the world, and secondarily, to follow up with Web visitors who download whitepapers or contact the sales team. Before Autopilot, they had some success doing so, but in an ad hoc manner. With Autopilot, they are able to gain specific user insights, and aggregate this data in Salesforce to create a dashboard that the global marketing lead can use to see how many people have activated the free application, how many have downloaded the whitepapers, how many sales calls are resulting in new leads, and how many of these are developing into new opportunities that the sales team is then following up and closing.

Having this information available in real time enables the head of marketing to identify bottlenecks or unqualified leads and to ensure timely follow-up. From there, she is able to build out the entire view from the initial customer touch point all the way through to her field sales team’s closing deal, and can make necessary changes after seeing the entire work flow in one place.

Pulling it all together

Marketing technology is unbundling: moving from central repositories to apps and a host of channels that fragment information and make it more difficult to track. Companies that are, and will be, successful with marketing automation are those that are able to embrace and organize the data that’s coming to them from these diverse sources. It’s going to take a new type of aggregation platform to make this happen: one that provides a central identity orchestration for the customer journey. In other words,the key will be reporting data from one location after synchronizing disparate information in a single, data-driven marketing automation platform.

Sponsored posts are content that has been produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. The content of news stories produced by our editorial team is never influenced by advertisers or sponsors in any way. For more information, contact sales@venturebeat.com.

]]>0Got data? How’s that working for you?How the wrong kind of customer health scoring can hurt youhttp://venturebeat.com/2015/07/31/how-the-wrong-kind-of-customer-health-scoring-can-hurt-you/
http://venturebeat.com/2015/07/31/how-the-wrong-kind-of-customer-health-scoring-can-hurt-you/#commentsFri, 31 Jul 2015 13:47:10 +0000http://venturebeat.com/?p=1777312GUEST: If your business relies on renewal revenue (and, with the rise of the subscription-economy, it very likely does), then you are most definitely familiar with customer health scores. They are critical to monitor to ensure that you’re delivering continuous value to your customers and growing your company. In fact, they are so important that many […]
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GUEST:

If your business relies on renewal revenue (and, with the rise of the subscription-economy, it very likely does), then you are most definitely familiar with customer health scores. They are critical to monitor to ensure that you’re delivering continuous value to your customers and growing your company. In fact, they are so important that many companies with a recurring revenue model are now building customer success teams tasked with ensuring customer retention and growing customer lifetime value.

A customer health score often directly measures the effectiveness of these teams, along with churn and renewal KPIs. But what’s the payback from that score? And how do you determine whether the score truly correlates to customer retention and renewal rates?

Moving from reactive to proactive health scoring

Whether it’s a traffic light model (red, yellow, and green indicators) or a 1-100 scoring metric to identify which customers are happy and which are at risk, too often, these types of customer health scores are based on subjective input and not on leading indicators of customer health. They are reactive versus proactive. They may work, but there is really no way to measure whether they do until it is too late and the customer has already churned. If you want to check if your health score is working before that happens, you need to use predictive analytics techniques.

There are many different predictive models that you can build to detect if your health score is a meaningful representation of customer health. Predictive lift is the key to understanding your health score when you have clean historical data that can be used to characterize behaviors and identify patterns. Predictive lift measures the performance of your health score against random guessing, or what the results would be if you didn’t use a health score at all. By using historical data where you know the outcome of the scenario, you can evaluate how well your health score works to predict certain behavior.

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If the predictive lift is high and actionable, the score has good economic value, because you’ll use it to apply resources to priorities that impact revenue retention and growth. But if the predictive lift of a health score is low, its economic value is low, because acting on the score is only slightly better than random or “gut-based” customer interactions.

Information value: the real key to meaningful health scores

In predictive analytics, we have the notion of “information value,” which quantifies how strongly a particular factor contributes to prediction. We find information value through a calculation involving the statistical correlation to an outcome. Information value is normalized to be anywhere from zero (i.e., the same predictive value as random choice) to 1 (i.e., perfect prediction). In practice, a factor that has an information value somewhere above 0.5 will provide good predictive lift and will correlate to decreased churn and improved renewal performance.

Let’s walk through some examples. When looking across its whole customer base, a given company might have 15 percent churn. So from a probability standpoint, if you were to randomly select a customer to approach as a retention risk, there’s a 15 percent chance that you’d actually be engaging an at-risk customer.

Predictive analytics can transform that 15 percent probability into a 50 percent probability or better, and that’s the goal of a health score — to increase the probability of engaging the right customer with the right interaction.

But what if you don’t know the information value of your health score? Suppose it has an information value of just 0.05? Using that health score, the probability that you’re engaging an at-risk customer rises to 21 percent — just a 6 percent lift above not using the health score at all.

A perfect prediction would identify 150 customers — i.e., the 15 percent out of 1,000 that are actually at risk. But with an information value of 0.05, 430 are identified as at risk, and only 90 of those are actually at risk. Consequently, 80 percent of customers flagged as unhealthy would actually be healthy — so you’d be spending time and resources on many customers who aren’t at risk. In addition, 40 of the actual at-risk customers are predicted to be healthy, so you’ll get unanticipated churn.

So what happens as the information value increases? At an information value of 0.4, the probability that a predicted churn would actually churn goes up to 36 percent. With an information value of 0.8, the probability gets to be 50 percent. At 0.8, the total number of identified customers is reduced from 430 down to 250, with only 23 at-risk customers going unidentified. A big improvement!

This is how we evaluate if a health score is working: Does it accurately focus resources where needed to effectively minimize churn and grow customer lifetime value?

With a high information value, it absolutely does. If your health score has a high information value, it is based on the leading indicators of churn — and you can reduce the amount of unnecessary intervention while increasing the coverage of at-risk customers.

The foundation for building your health scores

When you move beyond intuition or subjective input for health scoring and start relying on data, be sure to evaluate the information value of the factors you include, and don’t forget to back-test your final scoring mechanism. Just as importantly, start to think about different scoring mechanisms for different segments of customers. The best place to start is to think about taking control of your own data, and then create truly individualized health scores to match the expectations of your very individual customers.

Here are five specific steps you can take to build a solid foundation for customer health scores:

Step 1 — Break it down: Most health scores contain multiple factors, and to understand what action to take, you need the ability to drill down into different components of the score.

Step 2 — Look for trends and changes: Customer behavior changes over time, and some health scores do not reflect historical trends.

Step 3 — Zoom out for perspective: If you sell more than one service or product subscription, then lumping them together might hide important problems.

Step 4 — Put health scores in the context of your revenue lifecycle: By understanding where customers are in their journey, you can map actions to their expected business outcomes.

Step 5 — Automate the right customer interaction at the right time: You don’t want to wait for someone to review data and decide how to respond, as a health score doesn’t pay off unless you take action on it immediately.

Every company is different and will have a different approach, but at any company, high-level insight into customer health is good, and automated actions based on customer behaviors are even better. Use flexible analytics to monitor customer health and automated playbooks to keep customers on track. And, with in-depth insight and timely information, you can take in-person action as needed to deliver continuous customer value and keep customers for life.

]]>0How the wrong kind of customer health scoring can hurt you6 dashboards I use daily — and why every startup CEO should as wellhttp://venturebeat.com/2015/07/31/6-dashboards-i-use-daily-and-why-every-startup-ceo-should-as-well/
http://venturebeat.com/2015/07/31/6-dashboards-i-use-daily-and-why-every-startup-ceo-should-as-well/#commentsFri, 31 Jul 2015 11:10:27 +0000http://venturebeat.com/?p=1776951SPONSORED: This sponsored post is produced by Klipfolio. In order to succeed, we need to create value and grow. Sound familiar? In a fast-paced startup, the business environment is always in flux. You need to be able to course correct at a moment’s notice, and make changes to products, programs, campaigns, and internal activities based on […]
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SPONSORED:

This sponsored post is produced by Klipfolio.

In order to succeed, we need to create value and grow. Sound familiar? In a fast-paced startup, the business environment is always in flux. You need to be able to course correct at a moment’s notice, and make changes to products, programs, campaigns, and internal activities based on insight from your data. But it isn’t good enough to just make data available — you need to build a culture where everyone has metrics that they track and manage and that they are held accountable for.

At Klipfolio we do this using dashboards. Everyone in every department refers to dashboards daily, hourly, and in some cases minute by minute, to keep us on target. And the most important of these dashboards are displayed on 6 LCD TVs throughout our office. This provides motivation, keeps us focused on what is most important and drives accountability. They are also not just about monitoring metrics; when a number goes from green to red — we act.

In this post I’m going to share the metrics we monitor on six LCD TVs (we call them wall boards). Some are very specific to a cloud-based SaaS business but many apply to any business. I hope that you get inspiration from them.

Dashboard #1: My CEO’s Dashboard

My dashboard helps me track operational KPIs for every department in a single view. It gives me instant visibility into our progress towards key growth targets. As CEO of a SaaS startup, I track key business metrics such as total accounts, MRR, MRR per account, lead to win rates, and retention.

Each department contributes to these high level goals by achieving objectives such as number of visitors and leads (marketing); new wins and average MRR (sales); active users and account retention (UX); monthly burn rates (finances); product uptime (development); and new and open tickets (support).

This image below is an exact replica of my dashboard — but the data is not real. Ditto for all the other images in this post.

Click on the thumbnail for a larger image and to see all the metrics I monitor (and do the same with all the rest of the dashboards in this post.)

Dashboard #2: Sales

Sales at Klipfolio are all inbound, low asp, mostly credit card based and occurring 24X7. Given that, our operational dashboards monitor daily targets (as well as monthly totals) and if we are off track, all hands are on deck to understand and fix any problems.

Click on the thumbnails below to see the two sales dashboards we rotate on our wall boards:

Current sales performance dashboard: Our Customer Success team uses this dashboard to track current sales activities and attainment against goals.

Period sales performance dashboard: They use this one to track long-term trends, high-level business objectives, and deeper analysis of account acquisition trends.

Dashboard #3: Marketing

Like many of you, our marketing is continuously evolving and we’ve adopted the latest, greatest digital marketing tools available. As a result, our metrics — which change as we try new software and run new campaigns — are calculated by combining data from all those services. The team rotates three dashboards on their wall board:

Social Dashboard: Marketing uses this dashboard to track social engagement and conversion from social media sites.

Lead Generation Dashboard: We offer a free trial and converting our web traffic to a free trial is very important to us. Marketing uses this dashboard to track the effectiveness of our inbound activities towards this trial conversion goal.

Dashboard #4: Support

My support team uses their wallboard to monitor daily support tickets and documentation trends. By paying close attention to these metrics, they can see if we’re hitting our response time targets, if we are resourced appropriately, and where our users are looking for help.

Support ticket and response: This dashboard analyzes the ability of our support team to respond to customer tickets in a timely manner.

Tickets by type. Support uses this dashboard to get a view into the type of support tickets being submitted, and the impact of resolving those tickets.

Documentation Web Analytics: Support uses this dashboard to monitor the number of views of our knowledge base and our E-Learning modules.

Dashboard #5 UX

The UX team’s dashboard helps them monitor and improve our users’ experience within the product. Their goal is to provide customers and prospects with the best experience possible.

The UX team’s dashboard tracks metrics on a daily cadence against the backdrop of historic averages to account for oddities or sudden changes in the data. In terms of leading indicators, the UX team measures the number of daily active users and key user journeys. This dashboard plays a key role in aligning the UX and product teams around customer-facing initiatives. Sometimes the dashboard flags issues that are immediately actionable, while other times it’s used to provide context and real-time stats for strategic planning.

Dashboard # 6 Development

The development team uses their dashboards to monitor and take action on important resourcing and project KPIs, as well as to monitor application performance and uptime.

Development Dashboard: Development uses this dashboard to monitor the status of issues and features and the capacity of the team across all work items.

Dev-Ops: Development operations uses this dashboard to monitor our application performance and uptime — one of the key drivers of customer satisfaction.

Metrics evolve as you do

These dashboards/metrics have been months in the making and continue to be an iterative process. One of the first lessons we learned is that information needs to be put into context and that context changes as a startup evolves. It also changes depending on who’s looking at the information.

As the company establishes itself, it’s all about creating value. A startup will only succeed if it creates something clients want and need. So in the early stage of a startup, the CEO needs to monitor metrics that measure whether value is being created.

This means knowing how many people are using the product regularly, using common metrics such as daily, weekly, and monthly active users; and finding out how well the company is doing at retaining customers. You should track both customer retention and then net subscription retention so that you calculate both expansion and departing customers.

Monitoring revenue may not matter as much in the early days. Your job, at first, is to create a product that sells.

At some point, your metrics will tell you when to switch gears. As soon as you have evidence you are creating something of value, you will need additional information. You will shift from monitoring just value metrics to monitoring growth metrics.

You will need to know, for example, the cost of acquiring a customer and the lifetime value of a customer. Figures like that tell you how much you should be spending on marketing, promotion, and customer retention. This is not about value, it’s about efficiency and velocity.

You also want to look at revenue per employee. In the early stages of a startup, it may be acceptable to have relatively low revenue per employee. But as the company grows, this simple, but very telling metric needs to grow as well.

As we have shared our own experiences here, I hope it provides some insight into how we do things, and motivates you to manage your business through metrics. Do this, iterate, and stick with it, and you and your colleagues will make better decisions, more quickly.

I love hearing successes from other companies. If you monitor different metrics than we do and are willing to share, I’d love to hear from you at allan.wille@klipfolio.com.

Allan Willie is the CEO of Klipfolio.

Sponsored posts are content that has been produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. The content of news stories produced by our editorial team is never influenced by advertisers or sponsors in any way. For more information, contact sales@venturebeat.com.

Smartphones may have helped unleash the amateur creative in everyone, but there’s little question it has opened a Pandora’s box of bad photos. And this is where Gallery Doctor wants to help.

Above: Gallery Doctor

The handiwork of Israeli startup MyRoll, Gallery Doctor first launched on Android back in February. In a nutshell, the app scans your camera roll and iCloud and pulls out the ones that are blurry, duplicates, have bad lighting, or are just plain-old boring.

The problem it’s looking to solve — thousands of photos clogging up camera rolls and cloud-storage services — is universal. And that’s why it’s now available to iPhone users, too.

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Once the app has scanned your device, you can hit “Review” for each option (“Bad Photos,” “Similar Photos,” “For Review”) and in one fell swoop delete all the images identified by Gallery Doctor’s algorithms as “awry” — or you can manually peruse them one-by-one and assess them individually.

This is perhaps where Gallery Doctor differs from other similar apps like Cleen or Flic, insofar as Gallery Doctor can remove the manual labor by letting you delete all your “bad” photos with a single tap.

There’s no denying that Gallery Doctor has taken some cues from the aforementioned alternatives. Indeed, Cleen and Flic tap Tinder’s usability book by letting you swipe left or right to indicate whether you wish to keep a photo — it’s basically an easy way of cleaning up your camera roll.

Above: Gallery Doctor

With Gallery Doctor, there is a similar feature in its “For Review” section, which is where it highlights questionable photos–when it’s not certain whether they’re good, bad, boring, or what. Here, you can manually swipe to tell it what’s what.

Having already analyzed billions of photos through a separate gallery app which launched a few years back, MyRoll now has two main offerings — one for highlighting all your good photos, and one for highlighting all your bad photos.

Based on our tests, its algorithms are fairly accurate, though in the early stages you probably shouldn’t rely entirely on Gallery Doctor’s curation — there are definitely some that it deems “bad” that aren’t really “bad” (in this writer’s humble opinion, at least).

Using machine learning, however, Gallery Doctor should improve over time as it learns which kind of photos you like and don’t like.

Show me the money

Though the Gallery Doctor app for Android is free to download, with no ads or subscriptions in place either, MyRoll has opted to charge $3 to download the iPhone incarnation. We asked MyRoll founder Ron Levy about this disparity.

“We launched Gallery Doctor as a completely new product and we wanted to see how users react,” he says. “The reaction is fantastic with over 75 percent of users actually accepting our recommendations to delete photos. Since one of the ways we’re looking to monetize Gallery Doctor is through a paid app, we decided to check that first on iOS, given that the Android app is already free.”

In other words, MyRoll is exploring monetization options, and it hasn’t decided how to do so yet.

It’s worth noting that the main MyRoll app on Android (the iOS version was pulled a while back) charges to remove ads, and an additional Premium option is available with add-on services such as cloud storage. These are all potential options for both the Android and iOS versions of Gallery Doctor, so don’t be surprised if the iOS version eventually sheds its download fee.

Who takes the best / worst / most photos, anyway?

Gallery Doctor is actually a great little app, one that has garnered 500,000 downloads on Android already, analyzing 35 million photos along the way. And based on this data, it has pulled out some interesting nuggets that shed some light on trends and patterns among the smartphone-using public.

According to Gallery Doctor:

Geography

People in San Francisco take 1.5 times more bad photos than people in New York

People in Japan are the biggest photo takers, with an average of 2 times more photos in their gallery than those in the U.S., and 1.5 times more than those in Spain, Italy, and Germany

20 percent of Japanese users’ photos are near-duplicates

People in China snap the least amount of photos, about 50 percent fewer than the U.S.

Brazil, Argentina, and the U.K. are top for the worst photos

Gender & Age

Women take 10 percent more photos than men — 1,360 photos, compared to 1,230 for men

Women have an average of 20 percent more “bad” photos than men

Women are better at deleting — they remove 17 percent more photos than men

18-24 year-olds are the biggest photo-takers, taking an average of 30 percent more than 13-17 year-olds (who, as it happens, take the least amount of photos)

People aged 35-44 take the most similar photos out of all the age groups, almost 30 percent more than 18-24 year olds, and 10 percent more than 25-34 year olds

]]>0Gallery Doctor uses algorithms to free your iPhone from bad photosCustomer-centricity: How the digital wars will be wonhttp://venturebeat.com/2015/07/30/customer-centricity-how-the-digital-wars-will-be-won/
http://venturebeat.com/2015/07/30/customer-centricity-how-the-digital-wars-will-be-won/#commentsThu, 30 Jul 2015 11:10:46 +0000http://venturebeat.com/?p=1776184SPONSORED: Not only is it possible in the digital age, customer experience is once again the critical differentiator.
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Back in the golden era of the consumer age, it was commonplace to walk into a store and have a salesperson recognize you. He knew your name, your tastes, and even made personal recommendations. In the pre-internet days, stores like Selfridges and Nordstrom built their culture around customer service.

But then the Internet came along and things changed. Many companies began to neglect their customers. They turned instead to pushing product over experience. They had this idea — and many still do — that if you cast a wide enough net, some will buy. The result was a deluge of emails and ads. Few have moved away from this approach and the result is less omnichannel marketing and more omnichannel pestering. Annoyed customers unsubscribe, block ads, mark emails as spam, and ultimately, exit the sales funnel.

But retail is going back to its roots. Contrary to what some may think, personalized service in retail is not dead and gone. Not only is it possible in the digital age, but in a world where so many companies are selling similar products, customer experience is once again the critical differentiator.

According to Gartner, by 2018, companies that invest in personalization will outsell their competitors by 20 percent. And interestingly, nearly all companies (89 percent) surveyed by Gartner in a separate study said they believe customer experience will be their primary basis for competition by 2016. Retail thought leaders are heralding this new age, when in fact, it’s always been this way for consumers who never stopped believing that they are, in fact, always right; it’s just marketers and technology that are now catching up to being able to deliver on this digitally.

The three building blocks of customer centricity

A successful customer experience is all about relevancy — sending consumers content that is meaningful to them and coordinating messages across email, the Web, call centers, and other channels. In fact, when it comes to reaching consumers, relevancy is the new currency.

“Many businesses think they can send a mass campaign to everyone and that will be good enough, but data we’re analyzing from retailers proves otherwise,” explains Neil Capel, Founder and Chairman of Sailthru, a marketing personalization company. “You’ve got to deliver messages that are personalized, relevant, and at the right frequency, otherwise you annoy consumers, and they just leave. By focusing on creating a modern experience, brands can increase customer lifetime value by 20 percent and individual channel revenue, like email, by 70 percent.”

Customer centricity is founded on three essential building blocks — and all three need to work together to generate customer engagement and loyalty.

1. Get their individual attention

If you want to get a consumer’s attention, speak to her about the things she cares about. You don’t want to push children’s toys to someone whose children are out of the home. Likewise, you’ll have limited success pitching camping gear to someone with a Woody Allen-type phobia about the wilderness.

Get to know your customers as individuals, not as marketing segments. Learn about their preferences, hobbies, and favorite vacation spots. This requires a 360-degree view of the customer based on deterministic behaviors and interests from your direct interactions with each individual and using those insights to communicate deals and information that perk up their ears.

The Clymb, an Internet Retailer Top 500 company, recently increased email revenue by 71 percent and revenue per send by 175 percent by combining their email and onsite data to serve personalized email recommendations and advance segmentation by predicting which customers will purchase in the next 24 hours and 7 days. They’re proving that treating customers as individuals delivers significant revenue.

2. Add value

People like to feel like they’re getting their money’s worth, whether it’s buying something on sale or receiving exceptional customer experience. Good value equals a high quality product, an item that ships right away, and addressing customer concerns promptly without having to ask the same questions over and over — it converys that you know them and their history.

It also means standing behind your product, solving customer problems at every stage of the game, whether it’s in processing a return, locating a hard-to-find item, or understanding what a customer’s interests are so you can deliver personalized and appropriate offers in your ongoing marketing efforts.

3. Be consistent

Consistency is probably one of the biggest challenges brands now face. Today, consumers interact with brands through a multitude of channels and devices. A consumer might be watching an ad for a product on TV while simultaneously doing research on a laptop to learn more about that product or to find the best price.

Your job as a brand is to deliver an experience that is seamless across all those channels. Whether you’re engaging with a customer through your website, email, or push notifications from a mobile app, the messages you send out should be clear, consistent, and all based on a single view of the customer.

“It’s not about looking at this vast trove of data out there and trying to find the little signals that personalize the experience,” says Capel. “It’s about the depth and the quality of the data that exists on the individual customer and having the systems in place to act on it that makes a difference and creates competitive advantage.”

Putting your customers first means characterizing them as more than just a transaction. It means learning about each customer individually and basing a relevant, valuable, and consistent strategy around that. By fostering a dialog with customers, smart brands can ensure their customers will return again — and again.

Sponsored posts are content that has been produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. The content of news stories produced by our editorial team is never influenced by advertisers or sponsors in any way. For more information, contact sales@venturebeat.com.

]]>0Customer-centricity: How the digital wars will be wonGoogle Translate now provides instant visual translations in 27 languages on iOS and Androidhttp://venturebeat.com/2015/07/29/google-translate-now-provides-instant-visual-translations-in-27-languages-on-ios-and-android/
http://venturebeat.com/2015/07/29/google-translate-now-provides-instant-visual-translations-in-27-languages-on-ios-and-android/#commentsWed, 29 Jul 2015 13:00:39 +0000http://venturebeat.com/?p=1775816Google today announced that within the next few days its Google Translate app for iOS and Android will be able to give users immediate visual translations of text in 27 languages. Instant visual translation is now available for Bulgarian, Catalan, Croatian, Czech, Danish, Dutch, English, Filipino, Finnish, French, German, Hindi, Hungarian, Indonesian, Italian, Lithuanian, Norwegian, […]
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Google today announced that within the next few days its Google Translate app for iOS and Android will be able to give users immediate visual translations of text in 27 languages.

Join us at GrowthBeat where thought leaders from the biggest brands will share winning growth strategies on August 17-18 in San Francisco. Sign up now!

People using the app can try out the support for the new languages by downloading a language pack for each language. From there, the app can work even when the mobile device it’s running on has no Internet connection.

Google Translate can do this by relying on an increasingly trendy type of artificial intelligence called deep learning.

Google has trained its artificial neural network — a key technology for deep learning — on images showing letters as well as on fake images marred by imperfections, to simulate real-life scenes. From there, the Google Translate app looks up the letters in order to make an inference, or educated guess, about the words that the mobile device’s camera was pointed at.

Historically, this sort of complex processing would happen in a remote data center, scaled out onto several servers. But Google built a very small neural network and a carefully curated training data set. That way, the computing can happen on a mobile phone with limited processing power and little if any connection to the Internet. And that’s significant.

More information:

]]>0Google Translate now provides instant visual translations in 27 languages on iOS and AndroidOnboard right or die: 7 essential items to include in your First Time User Experiencehttp://venturebeat.com/2015/07/29/onboard-right-or-die-5-essential-items-to-include-in-your-first-time-user-experience/
http://venturebeat.com/2015/07/29/onboard-right-or-die-5-essential-items-to-include-in-your-first-time-user-experience/#commentsWed, 29 Jul 2015 11:10:31 +0000http://venturebeat.com/?p=1773444SPONSORED: Mobile apps must continually seek new ways to differentiate in a competitive market that is only getting fiercer by the second. Brought to you by Skyhook, this post is part of a series called “Apptitude” looking at how app owners can reduce friction, boost user engagement, monetize, and get to the user’s home screen. See all the […]
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SPONSORED:

Mobile apps must continually seek new ways to differentiate in a competitive market that is only getting fiercer by the second. Brought to you by Skyhook, this post is part of a series called “Apptitude” looking at how app owners can reduce friction, boost user engagement, monetize, and get to the user’s home screen. See all the posts here.

Mobile apps are increasingly seeking new ways to differentiate in a competitive market, reduce friction, boost user engagement, monetize and get to the user’s home screen. Some apps are already seeing these benefits thanks to in-store modes and location-informed content. The element of place will fundamentally change mobile apps for the better. Skyhook’s Context Accelerator unlocks the possibilities of place for apps with scalable geofencing and user personas that enable dynamic user experiences and relevant content.

When building out your app, the FTUX (First Time User Experience) is arguably the most important part of your user’s journey. Skip the steps below, and you’ll likely become just one of the many graveyard apps that litter people’s devices.

Right now, there are sixteen apps installed on my mobile phone that I have not used more than once. They were interesting enough to install but never made it past that point for me. Now they just take up space on my screen and will most likely be deleted soon.

App developers spend a lot of energy and money to get people to download their app, but that’s just half of the battle. Keeping users engaged is the next step and this is where the FTUX comes in to play.

What many overlook is that the FTUX is your only chance to make a good first impression. If you want your app to get to the user’s home screen you need to make this FTUX as compelling, simple, and useful as possible. If the FTUX is underwhelming, users will abandon the app without ever really digging into the features. But then there’s the counterpoint: according to Kahuna, when users are onboarded effectively, their lifetime value increases by up to 500 percent.

There is no one specific way to design a FTUX. Testing what works and experimenting with different orders and ideas will help you to optimize the FTUX. Here are some tips on what to include in this critical first impression.

1. Communicate the value

This is the most important thing to get across in the FTUX. The goal of onboarding is to have users see enough value in the app to willingly provide personal information to register. To achieve this, app developers and UX designers must clearly communicate the value of app features on the onboarding screens. Make it something unique to your brand’s mobile experience that will pique their interest.

2. Be transparent

As part of the FTUX, tell your users what kind of data you collect and why you collect the data. I know it’s crazy! Transparency coupled with paying off the use of the data with an insanely awesome experience tells them not only to keep things like location on, but to let it run in the background.

3. Make the call-to-action clear and simple

The call-to-action should be clear and it should go along with the value that the user will get if they click it. Make sure the CTA is not surrounded by other distractions that the user can click on or read — you want it to stand out.

Test different CTA language to see what is the most compelling to help users take the first step with your app.

4. Provide guided interaction

Create an environment that lets a new user learn by doing. Good guidance encourages the user to dig into the features of your app. Avoid passively explaining a feature in a way that takes the user out of the context of use.

5. Indicate advancement in the flow

Rather than throwing people blindly into a seemingly endless tour of your app, make it feel manageable by indicating progress within each step. Try showing users the number of screens or steps remaining, and how far they have advanced thus far. Providing an end point will help to encourage them to continue through and complete the process. Allow the option for users to skip the guided interaction if they wish.

6. Add location

When building an app, one of the best things you can do for your users is to make the experience easier. Many times this is achieved by reducing the steps they need to do to be able to get what they want — and knowing where your user is, and what they need when they’re there, saves them clicks and gestures, and time. And giving a user what they want quickly gives you another chance to engage with them.

Being able to deliver this simple and vital experience means you have to know a lot about what your user does. Collecting, analyzing and using contextual information like location behavior is the key to this reduction in friction. Talking about the value that location brings to users in the FTUX will increase the number of users who turn it on.

7. Establish a personal focus

In order to attract users and gain their loyalty, make sure the features and content you present are relevant to them. Users will not become engaged until the app is vital and relevant.

Provide this personalization through learning about the user as much as possible with location data. Tailor the user’s onboarding experience to his needs and behaviors. Continue to learn about the user through their engagement with the app and adapt to the user as his experience grows and changes.

Then watch your app make its way on to your users’ home screens.

Sponsored posts are content that has been produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. The content of news stories produced by our editorial team is never influenced by advertisers or sponsors in any way. For more information, contact sales@venturebeat.com.

]]>0Onboard right or die: 7 essential items to include in your First Time User ExperienceHow a $3.9B market is helping bridge the online and offline worldshttp://venturebeat.com/2015/07/28/how-a-3-9b-market-is-helping-bridge-the-online-and-offline-worlds/
http://venturebeat.com/2015/07/28/how-a-3-9b-market-is-helping-bridge-the-online-and-offline-worlds/#commentsTue, 28 Jul 2015 15:35:13 +0000http://venturebeat.com/?p=1774481VB EVENT: Digital marketing is a force of nature. Startups and established companies alike are using the online world to grow at rates never before imagined. But for many, the online world also causes negative knock-on effects in the offline world. If we all buy our products in cyberspace, where will that lead, and what will it mean for […]
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VB EVENT:

Digital marketing is a force of nature. Startups and established companies alike are using the online world to grow at rates never before imagined.

But for many, the online world also causes negative knock-on effects in the offline world. If we all buy our products in cyberspace, where will that lead, and what will it mean for businesses with a “real world” presence?

Fortunately, there is a lot happening to bridge the online and offline worlds, bringing fully integrated marketing and sales processes together so that both sides prosper. For companies that haven’t yet managed to connect the dots, we’ll be discussing ways to achieve this synergy during GrowthBeat in San Francisco next month.

Sponsored by VB

Join us at GrowthBeat where thought leaders from the biggest brands will share winning growth strategies on August 17-18 in San Francisco. Sign up now!

Take the smart beacon marketplace as an example.

In my recent report on consumer attitudes to personalization in marketing, I noted that the “Bluetooth smart & smart ready” market was worth $2.3 billion in 2013 and is expected to reach $3.9 billion by 2020. There’s a reason for this level of growth.

Marketing technology company Swirl Networks analyzed in-store campaign performance data from tens of thousands of shopper interactions over a three month period.

The results reveal that in-store beacon marketing campaigns are having a high impact on shopper behavior.

In particular, 60 percent of the shoppers studied opened and engaged with beacon-triggered content, and 30 percent of shoppers redeemed beacon-triggered offers at the point of purchase. These activities keep the shopper engaged, and increase the chance that they’ll buy right there, right then, rather than shopping around and taking their business elsewhere.

Truly integrated campaigns use offline shopping data to personalize offers for shoppers when they next go online.

Beacons aren’t just for retail applications, either.

Tealium has used smart beacons to better understand attendee behavior at conferences. It uses beacons at events with personalized messaging to help drive attendees to the right place at the right time, and togather important information on whether sessions, exhibitors, and other conference features are resonating or not. This offline data helps to customize online messaging, and ensure that attendees get the most from their conference.

But you don’t have to use smart beacons to connect online and offline data in interesting ways.

Meat Pack shows how location can be used to offer something exciting and surprising to its customers in a more personal way; on a smartphone.

Who is Meat Pack? A shoe store in Guatemala with over 115,000 likes on Facebook, the company is known for being trendy and edgy, and has a youth-based audience that lives online; specifically, on their smartphones.

In an innovative marketing campaign, Meat Pack used GPS data to determine if one of its customers was visiting a competitor’s store, which triggered a response on the customer’s smartphone.

In what was called a “hijack” campaign, the app flashed a discount offer on the mobile screen that started at 99 percent and counted down by 1 percent per second until the customer reached the Meat Pack store. In other words, the faster customers left the competition behind and got to a Meat Pack location, the more money they could save on a new pair of kicks.

The result? During the campaign, Meat Pack “hijacked” over 600 customers from competitors’ stores.

If you want to learn more about connecting offline and online marketing for optimal results, you won’t want to miss GrowthBeat. We’ll have informative, actionable insights from speakers such as:

More information:

]]>0How a $3.9B market is helping bridge the online and offline worldsGoogle search now lets you avoid lines by showing the busiest times at millions of places and businesseshttp://venturebeat.com/2015/07/28/google-search-now-lets-you-avoid-lines-by-showing-the-busiest-times-at-millions-of-places-and-businesses/
http://venturebeat.com/2015/07/28/google-search-now-lets-you-avoid-lines-by-showing-the-busiest-times-at-millions-of-places-and-businesses/#commentsTue, 28 Jul 2015 15:08:49 +0000http://venturebeat.com/?p=1775311Google now lets you avoid lineups.
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Google search today gained a brilliant new feature that has the potential to save you a lot of time. You can now see “popular times” (read: the busiest, most crowded, and most annoying intervals) for each day of the week when you look up a location.

In other words, it’s possible to avoid lineups and general overload at “millions of places and businesses around the world” just by googling wherever you want to go. For some locations, it’s obvious (going to the gym on Mondays and Tuesdays is always horrible) but in other cases, you might end up wondering when exactly you should leave work to get lunch, grab coffee, or go shopping.

Google offers an example. If you search for “Blue Bottle Williamsburg” and tap on the title, you’ll see how busy the cafe gets throughout the day:

This feature has the potential to be very useful. Keep in mind though, this is Google we’re talking about: The app may end up skewing the numbers.

If too many people start showing up at the least busy times, well, those will become the busiest times. We presume Google is hoping that won’t happen, and that people will instead start going to places at times spread out during the week, which would benefit consumers and businesses alike.

Sponsored by VB

Join us at GrowthBeat where thought leaders from the biggest brands will share winning growth strategies on August 17-18 in San Francisco. Sign up now!

We’re not sure where exactly Google is getting this data from, but we presume it is from location tracking in Google Maps on Android and iOS devices. While there are millions of Google Maps for mobile users in the world, not all of them use the app extensively.

In short, there are many reasons why “popular times” won’t give you the most accurate results, so don’t lean on it too much. That said, if you need to make a decision between two different days and times, it could be exactly what you need to tip the scales.

We’ve asked Google for more information about how this feature works and will update you if we hear back.

Update: Google pointed us to a blog post from August 2009 that details how the company gets its traffic data. This is the same technology:

When you choose to enable Google Maps with My Location, your phone sends anonymous bits of data back to Google describing how fast you’re moving. When we combine your speed with the speed of other phones on the road, across thousands of phones moving around a city at any given time, we can get a pretty good picture of live traffic conditions. We continuously combine this data and send it back to you for free in the Google Maps traffic layers.

In short, much like how Google computes traffic data using the anonymized aggregated movement of people on the road, the company is able to determine how busy a place is in the same way. Notice that there are no exact numbers provided: In both cases, the company just shows you what times and roads are busiest relative to other times and roads.

Join us for this live webinar on Wednesday, August 5 at 10 a.m. Pacific, 1 p.m. Eastern. Register here for free.

When you’re a startup, big budgets for large mobile ad campaigns don’t exist — and brand awareness through millions of ad impressions is far too lofty a KPI. It’s why Anne-Marie Kline, Senior Vice President of Marketing for the hair care and beauty startup Living Proof (which raised $30 million in series C funding two years ago), is laser-focused on mobile advertising that will result in concrete actions.

Using the power of social sites like Twitter, for example, Kline’s strategy is about qualifying consumers by providing content that they’re keenly interested in. “It might not have anything to do with Living Proof,” she explains, “but if they read an article about how to get the prefect cut, or tips for travel, or being a new mom, and they click on it, then we know that they’re interested in the topic area.”

The next step is to feed that interest with a follow-up piece of content that does refer to Living Proof. “We know then that [those consumers] are more likely to click on it, so we’re not just trying to get in everyone’s feed.”

Figuring out the mobile advertising ecosystem, and how best to leverage it, can be a maze. Kline’s been doing it for 10 years, with five years in real-time marketing. “And if you can do that at scale,” she says, “you become a relevant part of the conversation and not a bother or interrupter. You have a lot more impact on your consumer.”

Her insights will power a valuable part of the mobile advertising discussion at this essential webinar where we’ll dive into the entire ecosystem. We’ll explore the mobile adtech proliferation that’s exploded — but can be a mess of confusion. We’ll talk about the many tactics — video ads, location targeting, lookalike marketing, coordinated campaigns, native advertising, rewarded video, mobile programmatic — and what’s working and what’s not.

Pulling from the recent VB report on Brands and Mobile Advertising, VB’s VP of Research John Koetsier will take attendees through the most important findings. Along with reviewing what consumers are responding to most, he’ll dive into brands that are killing it in the mobile ad space, and how, for example:

Pandora achieved 4X engagement with its ads

A hardware retailer used video to get 3X brand recall over TV

The CW achieved 80 percent engagement with its mobile ad

Disney used interactive video to get 7X better engagement

What you’ll learn:

Strategies from major players in the mobile ad networks to increase engagement

The many ways fraud can wreak havoc on your mobile ad strategy

The best practices in mobile advertising

How to drive huge engagement with one mobile concept you’re probably not doing today

Speakers:

]]>0Mobile Advertising: Who’s winning and how (webinar)Taplytics adds new ‘smart push’ to get the right message to the right person at the right timehttp://venturebeat.com/2015/07/27/taplytics-adds-new-smart-push-to-get-the-right-message-to-the-right-person-at-the-right-time/
http://venturebeat.com/2015/07/27/taplytics-adds-new-smart-push-to-get-the-right-message-to-the-right-person-at-the-right-time/#commentsMon, 27 Jul 2015 18:33:25 +0000http://venturebeat.com/?p=1774594VB INSIGHT: The mobile marketing automation player is trying to go deep, not broad, in making the hundreds of daily interruptions that push generates useful rather than annoying.
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VB INSIGHT:

Mobile engagement platforms have sent over a trillion push notifications to people like you and me. Most of them, frankly, are mindless updates that somewhere, something has happened.

A few of them are useful and smart.

Increasing that few is something that Taplytics CEO Aaron Glazer is working hard on. Taplytics is an emerging player in the mobile marketing automation space with significant customers like Target, RBC, Shyp, and RetailMeNot. It is trying to go deep, not broad, in making the hundreds of daily interruptions that push generates useful, not annoying.

“Push is precious,” he said. “You need to get the right message at the right time to the right person in a way that enhances the relationship.”

Above: Push notifications are the most used feature of mobile marketing automation systems.

Smart push is messaging that is not just push, but notifications that are augmented with intelligence around location, relationship, purchase history, timing, and even your implied calendar: not just where you are, but where you’re going. An airline, for example, could send you a push message when you land, telling you which carousel your baggage will be at — but only if you actually checked bags. That’s critical, and that’s an example of providing value by upping the signal and reducing the noise.

A retailer — Frank & Oak, for example — would have very different messages for different clients. In Vancouver, where the primarily subscription-based retailer has an actual physical location, it’s one message. In San Francisco, where the company is a virtual resident, it’s another. The acquisition funnel is different, the customer relationship is different, and purchase pattern is different … and so is the relationship.

So the communication is, too.

Above: Taplytics’ CEO Aaron Glazer

Image Credit: LinkedIn

Taplytics’ new smart push service enables extreme levels of segmentation that allows what the company calls “hyper-personalization”: customization down to the individual level. That’s impressive — though I wonder whether companies would have the time or personnel to utilize it frequently. Messages can be based on “any combination of user data and in-app interactions,” the company said, including browsing activity and previous purchases.

“The importance of sending the right notification has gone up substantially … especially since the Apple Watch,” Glazer said.

(On the Apple Watch, of course, notifications are extremely terse, and should be even more relevant to immediate activity to justify the physical tap the wearer gets.)

Aside from simple updates on social networks, push messaging has typically been transactional and siloed. Glazer’s goal is to change that and make it part of a conversation that brands have with you, a relationship that goes far beyond using their apps … while utilizing all the data that the brand has about you. Assuming you want that, of course. And assuming the companies you patronize care enough to make it happen.

Otherwise, push messages are just noise:

“They’ll backfire 100 percent of the time if they’re generic and untargeted,” said Glazer.

More information:

]]>0Taplytics adds new ‘smart push’ to get the right message to the right person at the right timeMobile Marketing Automation Pt V: Getting right time, right place right. (webinar)http://venturebeat.com/2015/07/27/mobile-marketing-automation-pt-v-getting-right-time-right-place-right-webinar/
http://venturebeat.com/2015/07/27/mobile-marketing-automation-pt-v-getting-right-time-right-place-right-webinar/#commentsMon, 27 Jul 2015 14:45:51 +0000http://venturebeat.com/?p=1773549VB WEBINAR: Right time. Right place. It's the mantra for the mobile age, and marketers who are leading the pack are evolving the way they engage consumers to be in front of them when and where it matters most.
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Right time. Right place. It’s the mantra for the mobile age, and marketers who are leading the pack are evolving the way they engage consumers to be in front of them when and where it matters most. Take Viggle. The entertainment rewards platform enbles entertainment companies — televison networks and shows, music companies, and even book publishers — to use Viggle to reward their customers for interacting with their content. The more customers engage, the more points they earn, the more rewards they get.

“Our marketing automation comes down to a lot of science that we use internally,” says Viggle’s VP of Marketing Julie Gerola. “A metric we’re really proud of is our ability to keep people in the app nearly an hour per session when most apps are probably lucky to keep people on board in the neighborhood of two to five minutes.”

One of Gerola’s biggest accomplishments is bringing back lapsed users — the users who have installed and registered, and used the app for a while before it became just another forgotten app on their device. But as Gerola says, “I’m happy to spend a bit of money to keep users who may be falling off to come back and wake up, rather than constantly filling the funnel with new users.” By sharpening their tools with laser targeting, Viggle now is winning back 10 percent of all users targeted, whereas just two to three months ago, that rate was at 2 to 3 percent.

How do they do that? Right time. Right place. A user is on their phone, tweeting about a show they’re consuming at that very moment — and at that very moment (because Viggle knows from past, and lapsed use, that this user is a fan of that show) — Viggle serves up an ad, letting them know that they could be earning Viggle points by doing what they’re doing right now.

Still, despite the success with lapsed users, Gerola believes that, “From a marketing automation point of view, you can see a ton of opportunity. It shouldn’t just be about lapsed activity, it should be about the positives as well.” She’s referring to keeping users invigorated before any kind of retention problem develops. “It’s about sustaining engagement rate over a significant period of time. There’s a lot of opportunity there, especially in a mobile environment.”

Viggle is also expanding beyond entertainment and working with retailers to drive consumers in-store. For example, they’re now working with Walmart who in turn might be partnering with a CPG brand, say P&G, on a particular promotion for a specific product. Using beacon technology partner InMarket, once a consumer is in proximity, a Viggle customer will get a notification letting them know they can earn points for checking out the product. “And you don’t even have to make the transaction at that point of time,” explains Gerola. “But we’ve been able to prove our ability to not only drive that foot traffic to the store, but in addition to translate it into a purchase.”

Despite successes like these, the adoption rate of mobile marketing automation is very low: just 1.5 of businesses today are using mobile marketing automation. To help marketing leaders understand its potential, and how to leverage it, VB’s latest report on MMA analyzed 23 of the top MMA vendors, surveyed 375 app publishers and developers totaling almost 9,000 apps — and looked at which brands are doing MMA well, and how.

In this webinar, VB’s VP of Research John Koetsier will be joined by Gerola. Koetsier will begin by taking attendees through the most important highlights of the research report — including which MMA features and tactics are working best and who’s doing it — while Gerola will share insights from the trenches.

Mobile-first has become an undeniable reality. This webinar will help your company leverage that reality to become a mobile-first leader.

Like our faces, our words can reveal what we feel. Today, Pennsylvania-based BehaviorMatrix is launching the next-generation of its platform to better understand the emotions behind our communications and decisions.

The company said this newest incarnation, called SMARTview360, moves “emotional signal detection” beyond its previous foundational version by enabling contextual analysis, such as tracing an emotional reaction through a series of conversations or statements from various sources.

“You declare the context,” CEO and cofounder Bill Thompson told me, “and the system looks at that view.” For instance, the platform can “map out obscure networks of people” who are driving the emotional content of discussions, and “understand who is the real influencer.”

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The idea is not only to know what people are saying, but why they’re feeling that way, what’s influencing them, and what are the emotional reasons behind their opinions and decisions.

The context can even include what emotional triggers might induce specific kinds of people to do certain things in the future, using predictive analytics.

“Decisions are based on emotions,” chief product officer Keith Harry told me. “If we can understand why people are making those decisions, we can predict behavior.” BehaviorMatrix claims an unspecified “high degree of accuracy” for its emotional and predictive analyses.

Thompson told me the platform’s Emotional Signal Processing (ESP — get it?) is the first of its kind, based on concepts contained in a 2014 patent the 2012-founded company has obtained. It inhales written communications from three million different sources, including social media, news outlets, blogs, forums, surveys, and even transcribed broadcast TV and offline materials, like print newspapers.

Uses include customer service improvement, new product development, reputation management, brand awareness, and the monitoring of campaign effectiveness.

One unnamed client, Thompson said, conducted an emotional analysis of reaction to a new character for an ad campaign in TV and other media. SMARTview360 found that the character was actually causing harm to the brand because of the character itself and the frequency with which it was being shown.

“They were annoying the audience,” he said, and “decreasing the emotional brand equity.”

Although Thompson wouldn’t specify which companies use his platform, he indicated that the company’s headquarters in Blue Bell, Pennsylvania — smack in the heart of the pharmaceutical industry — gives a hint as to a key set of customers.

Pharmas, he pointed out, want to know the emotions surrounding “disease state journeys” — and, presumably, what emotional triggers in direct-to-consumer advertising will get patients to seek their drugs. Other industries using the platform include media and entertainment, finance, and government, the company said.

But not political organizations, which the company used to service but doesn’t anymore because, Thompson said, “we’re not political.”

SMARTview360, according to BehaviorMatrix, goes way beyond the sentiment analysis that is common on social monitoring platforms like Spredfast, Meltwater, or Hootsuite. There is a natural language processing engine that processes language in ways similar to the human brain, and it can apparently understand the emotional context behind a sentence like: “The last Mitsubishi I bought new died before my trusty ten-year-old Honda.”

Thompson added that the platform understands such subtleties as sarcasm, irony, or spam, and can read behind the inarticulate phrasing that has led more than one digital communication, without the tonality of a human voice, to be misinterpreted. In addition to English, it can parse the emotion in Chinese, Arabic, or German communications.

There are data science platform competitors, he said, like Palantir and Data-Miner, although neither is involved in emotional analysis. He also pointed to IBM’s Watson, which is “experimenting with the tone of messaging.”

But “we already have that,” he said, “plus we don’t need a supercomputer.”

]]>0BehaviorMatrix wants to understand the fear, happiness, or anger behind your choicesThe importance of a robust QA process and in-game analytics: rush either and you’ll be sorryhttp://venturebeat.com/2015/07/27/the-importance-of-a-robust-qa-process-and-in-game-analytics-rush-either-and-youll-be-sorry/
http://venturebeat.com/2015/07/27/the-importance-of-a-robust-qa-process-and-in-game-analytics-rush-either-and-youll-be-sorry/#commentsMon, 27 Jul 2015 11:10:24 +0000http://venturebeat.com/?p=1770271SPONSORED: "A delayed game is eventually good, but a rushed game is forever bad." Unfortunately, today's game developers have forgotten this sage advice and are paying the price.
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SPONSORED:

This sponsored post is produced in association with XIM Wireless.

“A delayed game is eventually good, but a rushed game is forever bad.” That quote belonged to legendary game designer Shigeru Miyamoto, creator of Super Mario Bros., The Legend of Zelda, and other popular Nintendo games. Unfortunately, today’s game developers have forgotten this sage advice and are paying the price.

Take for example the recent release of Batman: Arkham Knight. The anticipated final chapter in the Batman video game series from Rocksteady received rave reviews for the PlayStation 4 and Xbox One versions. However, the PC version currently plays like a mess.

Warner Bros. Interactive entertainment, the game’s publisher, suspended digital sells of the PC version and issued refunds, because of how unpolished it was. By rushing out an unplayable version of Arkham Knight for PC, Warner Bros. not only lost money, but lost the trust of its audience as well.

Arkham Knight isn’t alone. Sega received ridicule when it released Sonic Boom for Wii U last year, which featured a plethora of bugs and glitches. Mobile apps aren’t immune either, as buggy games are commonly released on iOS and Android. With the mobile market flooded with games, a bad first impression is all it takes to doom a potentially successful app.

Making the QA process top priority

Alex Rechevskiy, game producer at XIM Wireless (part of XIM, Inc.), explained why QA should be top priority to developers.

“During production, QA is one of those elements of the development cycle that is very easy to sacrifice in the name of ‘hitting the deadlines,” Rechevskiy told VB. “Even some experienced teams end up cutting the QA process substantially during a crunch. However, it is well known that skipping or shortchanging this final step in the development cycle will inevitably cost the team more time, money, and potentially even bring disastrous consequences in terms of the loss of long-term customer loyalty.”

Customer loyalty is especially important in the age of GameStop, where a dissatisfied customer could buy his next game used. It’s also important to maintain trust when trying to sell audiences on downloadable game content later on. PC gamers missing out on the superior version of Arkham Knight aren’t likely to buy $40 of DLC.

Rechevskiy said it’s imperative for teams to implement a robust QA process as part of the development cycle — not separate from it — to avoid the “temptation” of cutting down QA at the end of the project. He stressed that teams should automate the process, or at least follow a specific regime of QA actions that are essentially “on rails,” to prevent any deviations from members.

If a proper QA process is not established, Rechevskiy said it could financially cost the company. He warned rolling out updates won’t always fix problems within a game, as those updates are often hastily released, introducing more bugs than fixes. “In the gaming space, specifically, the userbase is extremely vocal, and hands out 1-star reviews for the slightest misstep.”

Maximizing profits with in-game analytics

Another important factor for developers to consider, when it comes to maximizing revenue, is in-game analytics.

“It is a well known fact in today’s market that in the F2P (free-to-play) gaming space (the business model currently dominating all mobile markets), only a small percentage of users ever monetizes,” Rechevskiy states. “This exacerbates the need for a proper in-game analytics system — the margin for error is very small, and an intelligent game developer will focus its efforts on specifically identifying the behavior of those players that do pay, and then improving and streamlining those players’ gameplay experience to extract the maximum value from each such payer.”

Rechevskiy suggests improving monetization in games by offering special discounts to users who purchased in the past; rewarding them for their past service. Another suggestion is to offer certain users the option to buy at reduced price at the exact moment they’re likely to pass on the offer, based on past data about purchases from the userbase.

XIM has found that effective in-game analytics can help increase MAU by 212 percent, mobile app revenue by 150 percent, and app lifecycle by 200 percent. When we asked Rechevskiy how companies can hope to achieve these stats, he had this to say:

“In today’s gaming space, it is virtually impossible to reach or maintain profitability without an effective in-game analytic system. There are many situations where the in-game analytics solutions can provide actionable data for the development team, including on-boarding funnels, user segment analysis, and level progression tracking system.”

Examples Rechevskiy listed include using in-game analytics to adjust difficulty in a level or tutorial step and observing how players use social media to spread word of the game.

In-game analytics could be seen as an extension of a robust QA process. Further analyzing a game’s quality, guaranteeing customers stay satisfied for years to come.

Sponsored posts are content that has been produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. The content of news stories produced by our editorial team is never influenced by advertisers or sponsors in any way. For more information, contact sales@venturebeat.com.

]]>0The importance of a robust QA process and in-game analytics: rush either and you’ll be sorryWatch this brilliant visualization of words in the English languagehttp://venturebeat.com/2015/07/26/watch-this-brilliant-visualization-of-words-in-the-english-language/
http://venturebeat.com/2015/07/26/watch-this-brilliant-visualization-of-words-in-the-english-language/#commentsSun, 26 Jul 2015 19:54:34 +0000http://venturebeat.com/?p=1774220Can you guess the three most frequently used English words?
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What are the most frequently used words in the English language? You’re about to find out.

In December 2012, when he was 85 years old, Mayzner contacted Peter Norvig, a research director at Google. He wanted to see if “perhaps your group at Google might be interested in using the computing power that is now available to significantly expand and produce such tables as I constructed some 50 years ago, but now using the Google Corpus Data, not the tiny 20,000 word sample that I used.”

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Norvig did exactly that, and today, YouTube user Abacaba created a brilliant visualization of the results. Before you start watching, try to guess the three most frequently used words in the English language.

Got your three words? Good. Here we go:

I guessed “the” correctly, but my second and third place guesses were “a” and “I” — neither of which are even in the top five. It turns out that “a” is sixth and “I” is 19th!

Here are the top 50 most frequently used words in English:

If the video piqued your interest but didn’t quench your thirst for knowledge, you might want to know how Norvig put it all together. In his own words:

I consulted the Google Books Ngrams raw data set, which gives word counts of the number of times each word is mentioned (broken down by year of publication) in the books that have been scanned by Google.

I downloaded the English Version 20120701 “1-grams” (that is, word counts) from that data set given as the files “a” to “z” (that is, http://storage.googleapis.com/books/ngrams/books/googlebooks-eng-all-1gram-20120701-a.gz to http://storage.googleapis.com/books/ngrams/books/googlebooks-eng-all-1gram-20120701-z.gz). I unzipped each file; the result is 23 GB of text (so don’t try to download them on your phone).

I then condensed these entries, combining the counts for all years, and for different capitalizations: “word”, “Word” and “WORD” were all recorded under “WORD.” I discarded any entry that used a character other than the 26 letters A-Z. I also discarded any word with fewer than 100,000 mentions. (If you want, you can download the word count file; note that it is 1.5 MB.)

I generated tables of counts, first for words, then for letters and letter sequences, and keyed off of the positions and word lengths.

Keep in mind that these results are based on Google Books data of 97,565 distinct words, which were mentioned 743,842,922,321 times. That is 37 million times more than in Mayzner’s 20,000-mention collection.

]]>0Watch this brilliant visualization of words in the English languageMalcolm Gladwell: the Snapchat problem, the Facebook problem, the Airbnb problemhttp://venturebeat.com/2015/07/24/gladwell-on-data-marketing-the-snapchat-problem-the-facebook-problem-the-airbnb-problem/
http://venturebeat.com/2015/07/24/gladwell-on-data-marketing-the-snapchat-problem-the-facebook-problem-the-airbnb-problem/#commentsFri, 24 Jul 2015 17:25:32 +0000http://venturebeat.com/?p=1773509Last night futurist, journalist, prognosticator, and author Malcolm Gladwell told pretty much the most data-driven marketing technologist crowd imaginable that data is not their salvation. In fact, it could be their curse. “More data increases our confidence, not our accuracy,” he said at mobile marketing analytics provider Tune’s Postback 2015 event in Seattle. “I want to puncture […]
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Last night futurist, journalist, prognosticator, and author Malcolm Gladwell told pretty much the most data-driven marketing technologist crowd imaginable that data is not their salvation.

In fact, it could be their curse.

“More data increases our confidence, not our accuracy,” he said at mobile marketing analytics provider Tune’s Postback 2015 event in Seattle. “I want to puncture marketers’ confidence and show you where data can’t help us.”

The Snapchat problem

The average person under 25 is texting more each day than the average person over 55 texts each year, Gladwell says. That’s what the data can tell us.

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Developmental change, in Gladwell’s story, is behavior that occurs as people age. For instance, “murder is a young man’s game,” he said, with almost all murders being committed by men under the age of 25. Likewise, dying in a car accident is something that just “statistically doesn’t happen” over the age of 40. In other words, people age out of developmental changes — they are not true long-term lasting shifts in behavior.

Generational change, on the other hand, is different. That’s behavior that belongs to a generation, a cohort that grows up and continues the behavior. For example, Gladwell said, baby boomers transformed “every job in America” in the ’70s as they demanded more freedom, greater rewards, and changes in the boss-employee relationship.

The question is whether Snapchat-style behavior is developmental or behavioral.

“In the answer to that question is the answer to whether Snapchat will be around in 10 years,” Gladwell said.

The Facebook problem

Facebook is massive, amazing, and almost literally incredible: a social network connecting over a billion people. That’s what the data can tell us.

What it can’t tell us is what it will become — what its full upside potential could be.

“Facebook is at the stage that the telephone was at when they thought the phone was not for gossiping — it’s in its infancy,” Gladwell said, referencing that the early telephone marketers thought the phone was only for business. “We need to be cautious when making conclusions … we can see some things now, but we have no idea where it’s going.”

Why?

The diffusion of new technologies always takes longer than we would assume, Gladwell said. The first telephone exchange was launched in 1878, but only took off in the 1920s. The VCR was created in the 1960s in England, but didn’t reach its tipping point until the 1980s — over and above the vociferous opposition of the TV and movie industry, which was convinced it would destroy their business.

And that’s for technologies that are just innovative.

Technologies that are both innovative and complicated, like Facebook, take even longer to really emerge.

“Any kind of new and dramatic innovation takes a long time to spread and be understood,” Gladwell said. “If we look at history, it tells us that the Facebook of today looks almost nothing like what it will tomorrow.”

The Airbnb problem

The sharing economy, featuring companies like Airbnb, Uber/Lyft, even eBay, rely on trust. And they’re growing and expanding like wildfire.

And yet, if you look at recent polls of trust and trustworthiness, people’s — and especially millennials — trust is at an all-time low. Out of ten American “institutions,” including church, Congress, the presidency, and others, millennials only trust two: the military and science.

That’s conflicting data. And what the data can’t tell us is how both can be true, Gladwell said.

“Data can tell us about the immediate environment of people’s attitudes, but not much about the environment in which they were formed,” he said. “So which is right? Do people not trust others, as the polls say … or are they lying to the surveys?”

The context helps, Gladwell said.

That context is a massive shift in American society over the past few decades: a huge reduction in violent crime. For example, New York City had over 2,000 murders in 1990. Last year it was 300. In the same time frame, the overall violent crime index has gone down from 2,500 per 100,000 people to 500.

“That means that there is an entire generation of people growing up today not just with Internet and mobile phones … but also growing up who have never known on a personal, visceral level what crime is,” Gladwell said.

Baby boomers, who had very personal experiences of crime, were given powerful evidence that they should not trust. The following generations are reverting to what psychologists call “default truth.” In other words, they assume that when someone says something, it’s true … until they see evidence to the contrary.

More information:

]]>0Malcolm Gladwell: the Snapchat problem, the Facebook problem, the Airbnb problemLessons from Zynga: Data is essential, but it shouldn’t rule your worldhttp://venturebeat.com/2015/07/23/lessons-from-zynga-data-is-essential-but-it-shouldnt-rule-your-world/
http://venturebeat.com/2015/07/23/lessons-from-zynga-data-is-essential-but-it-shouldnt-rule-your-world/#commentsFri, 24 Jul 2015 01:00:13 +0000http://venturebeat.com/?p=1770557GUEST: Although Zynga’s focus on metrics led to extremely successful growth and revenue initially, some Zynga alumni think the company may have been too data-driven.
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GUEST:

If you’re on Facebook, chances are you’ve either played or been invited to FarmVille, CityVille, or Mafia Wars — three of Zynga’s most popular games. Zynga pioneered social gaming as we know it today, setting the tone for successors like Candy Crush and Temple Run. Zynga is much more than a casual gaming company though – it’s a data and analytics powerhouse that has revolutionized data-driven product development and optimization.

Because I’ve bet my career and my company on using data to understand user behavior, I’ve followed Zynga with great interest for years. What its product and analytics team did years ago is what any mainstream company with a mobile app or web site needs to do today to maintain a competitive edge and drive growth.

Zynga product developers understood the importance of robust user analytics from the beginning. While other gaming companies were only looking at basic counters, Zynga recognized that it was user behavior that really needed to be understood in order to spark viral engagement. This unconventional approach to analytics helped catapult Zynga from zero to hero almost overnight.

Tracking user behavior at a granular level was a fairly new concept when Zynga launched. At the time, there weren’t any analytics platforms that met their needs, so Zynga’s engineers built their own analytics infrastructure. Zynga’s original approach to analytics has inspired some of the brightest minds in technology to craft their analytics models in the same way, applying lessons they took from Zynga to industries beyond social gaming.

Here’s what you can learn from Zynga that has nothing to do with farm animals or the mob.

1. Create analytical models before launch to measure expectations

Before launching any Zynga game, the team built an analytical model for the game’s performance. The model examined factors like the viral hooks built into the product and the user acquisition channels. From this data, the model attempted to predict key metrics, including the number of new installs per day and how that would decay over time, virality K-factor over time, Day 1 retention over time, and revenue per daily active user.

Teams used these models as a basis for understanding how to engineer the game for maximum growth, engagement, and revenue. These models also provided a baseline for comparison, so that as soon as the game launched, the team could quickly gauge whether or not it was on track to meet expectations.

2. Don’t chase short-term gains at the expense of durable growth

Having too narrow a focus on short-term gains can cost you long term revenue, as was the case with Zynga. A prime example of this was its ‘flash sales’ campaign. In Zynga games, there are virtual goods for a set price. The first time Zynga ran a sale on these, it was immensely successful. Revenue on that day shot through the roof.

In the short term, the data suggested that sales were a great driver of revenue — more users were converting to buyers. But eventually, the strategy backfired. Users came to expect the sales, and Zynga found itself running bigger sales more frequently to get users to buy more virtual currency. Over the long term, the sales strategy was a net negative, as users had more currency than they could spend, and sales became less and less effective.

3. Data shouldn’t rule your world

Zynga didn’t ascend the social gaming throne through the content of its games. In fact, Zynga’s former VP of Analytics, Ken Rudin, is famously quoted as saying that Zynga was “an analytics company masquerading as a games company.”

Although Zynga’s focus on metrics led to extremely successful growth and revenue initially, some Zynga alumni think the company may have been too data-driven. The flash sales backfire is one example of how looking purely at the numbers led to the wrong decision.

Former Zynga product manager and current cofounder of Rocket Games, Niko Vuori, recently recounted to me that Zynga’s laser focus on metrics may have been one of the main reasons it missed out on things that are more difficult to measure, though just as crucial, like improving the overall usability of a game. “What ended up happening is people were exceptionally focused on the data and didn’t spend enough time looking at the qualitative gameplay,” said Vuori. “The main thing I think a lot of us have taken from Zynga is that data has its place, it helps you make decisions, but you should still be open to doing things that are different, that are gut-driven. Data should not rule your world.”

Roy Sehgal, an early vice president and general manager at Zynga who is now an investor in my company, told me how important it is to have good hypotheses before diving into the data. “Data-driven is a loaded term,” he said. “I believe you need to be hypothesis-driven and use data to validate (or invalidate) your hypotheses. The data identifies where your hypotheses were right or wrong and highlights areas of potential improvement to the user experience.”

Although they made some mistakes along the way, Zynga’s metrics-driven culture allowed it to standardize social gaming while simultaneously introducing the world to data-driven product development and optimization. Today, with so much data being captured through the web and on mobile, companies in every industry are more capable than ever to turn user data into their most powerful business tool.

Spenser Skates founded mobile event-based analytics startup Amplitude in 2012 with cofounder Curtis Liu. Prior to that he founded text-by-voice Android app Sonalight and worked as a Algorithmic Trader for DRW Trading Group.

]]>0Lessons from Zynga: Data is essential, but it shouldn’t rule your worldPersonalized marketing: What works, what doesn’t — and what consumers love and hate (webinar)http://venturebeat.com/2015/07/23/personalized-marketing-what-works-what-doesnt-and-what-consumers-love-and-hate-webinar/
http://venturebeat.com/2015/07/23/personalized-marketing-what-works-what-doesnt-and-what-consumers-love-and-hate-webinar/#commentsThu, 23 Jul 2015 15:44:33 +0000http://venturebeat.com/?p=1772372VB WEBINAR: Customers expect and demand that their needs and preferences are known and responded to -- and companies who are doing that are winning.
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Forgetting someone’s name in the real world happens. We forgive. We move on. In the world of online commerce and marketing, customers are not so forgiving.

This is the age of personalization — and we’re talking about a lot more than first names. Customers expect and demand that their needs and preferences are known and responded to — and companies who are doing that are winning. They’re doing it with hyper-personalized data such as their customers’ location, device preference, channel usage, product affinity, time-of-day usage, and what interests them in the wide world at large.

But as impressive as these numbers are, racing to embrace personalized marketing without knowing the landscape can easily backfire. Consumers can love you for remembering who they are one moment, and resenting an invasion of privacy the next. They’ve become increasingly aware that every click and swipe is being tracked, and the potential creep factor is nothing to trivialize.

At that same time, there’s still a big learning curve for many marketers playing catch-up as personalization is more and more becoming table stakes. What data is most effective? What’s safe to use and won’t turn off consumers? What drives consumers to provide personally identifiable information? Consider just some of the many types of data that can be used for personalization:

Purchase/commerce history, including products/services bought, and product/services looked at

In this not-to-missed webinar, VB Insight Director of Marketing Technology Stewart Rogers will take you through the essential findings of the report, which surveyed over 1,700 consumers as well as experts, vendors, and commentators. And for some real-world insights into how personalization can be applied with outstanding results, Rogers will be joined by Preeti Kelapure of the career community Glassdoor.

What you’ll learn:

What types of personalization are acceptable, and which to avoid

The correct process for on-boarding personalization, and how to manage when consumers opt out

What the future of personalization looks like, for both B2C and B2B organizations

The rules and regulations at play

The marketing technologies that will save all marketers from crossing the “uncanny valley” into “Creepyville”

Speakers:

]]>0Personalized marketing: What works, what doesn’t — and what consumers love and hate (webinar)Predictive lead scorer Leadspace, founded on data tactics used against terrorists, captures $18Mhttp://venturebeat.com/2015/07/23/predictive-lead-scorer-leadspace-founded-on-data-tactics-used-against-terrorists-captures-18m/
http://venturebeat.com/2015/07/23/predictive-lead-scorer-leadspace-founded-on-data-tactics-used-against-terrorists-captures-18m/#commentsThu, 23 Jul 2015 11:30:36 +0000http://venturebeat.com/?p=1772100Many predictive lead scoring companies promise to find new prospects for your company. But not many predictive lead scoring companies were founded by a former Israeli military intelligence expert who applies anti-terrorist data mining tactics to help companies better understand their customers. Today, that B2B company, San Francisco-based Leadspace, is announcing it has scored $18 […]
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Many predictive lead scoring companies promise to find new prospects for your company.

But not many predictive lead scoring companies were founded by a former Israeli military intelligence expert who applies anti-terrorist data mining tactics to help companies better understand their customers.

Today, that B2B company, San Francisco-based Leadspace, is announcing it has scored $18 million in new funding so it can continue to figure out signals left by individuals in big data.

The signals, CEO Doug Bewsher told me via email, are determined from behavioral, intent, and other data in the open Web and social media so that businesses can find people who make buying decisions, and then create targeted messages that help close deals.

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Founder Amnon Mishor had been a member of Israel’s elite 8200 military intelligence unit, and is today the firm’s chief technical officer. Bewsher is the former chief marketing officer for both Skype and Salesforce.

The company claims more than 100 current customers, including Oracle, Autodesk, and Microsoft, and it says clients generate $100 worth of pipeline-filling deals for every dollar spent with Leadspace.

Bewsher told me the competitors his company most often encounters are Lattice and Infer. One differentiator, he said, is that Leadspace “both enriches and scores leads.”

“Where most other providers focus only on the scoring algorithms,” he emailed, “Leadspace enriches lead data first with web and social sources to improve scoring accuracy. Beyond enrichment and scoring, Leadspace also finds net new leads (discovery), which is essential when entering new markets or looking to expand.”

His company “goes beyond company-level information to the individual level,” he said, with attention to both aspects, as well as to each separately. Leadspace also builds up company-level attributes from individual info, he said, instead of the other way around.

And Leadspace utilizes a unique “hybrid approach that takes into account both historical data and user input,” he said, meaning a customer company could build a predictive scoring model based on its own info, with little or no data, such as for new markets or for new products.

Led by Battery Ventures, this Series B round included investments from JVP and Vertex. It brings the total raised thus far to $35 million, including debt.

It will be used to increase Leadspace’s global footprint, extend its partnerships, and further develop its platform by expanding its tech development team in Israel. New integrations will be developed for marketing platforms like HubSpot and Pardot, employing the company’s recently launched API.

Bewsher said the company will also expand into new engagement opportunities, like advertising or content, so the platform’s customers can have “more options for finding and connecting with their ideal buyers.”

More information:

]]>0Predictive lead scorer Leadspace, founded on data tactics used against terrorists, captures $18MStop hiring data scientists until you’re ready for data sciencehttp://venturebeat.com/2015/07/22/stop-hiring-data-scientists-until-youre-ready-for-data-science/
http://venturebeat.com/2015/07/22/stop-hiring-data-scientists-until-youre-ready-for-data-science/#commentsThu, 23 Jul 2015 01:00:55 +0000http://venturebeat.com/?p=1772249GUEST: If you thought you were ready for data science but you're not, don't put your new data science hire into a different role or block them as they keep trying to be successful. Be honest and let them go.
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GUEST:

I had yet another call last week with a brilliant data scientist working inside of a Human Resources Department of a major business. This HR data scientist has both a strong analytics and predictive analytics background. She has a Bachelor’s degree in statistics and a Master’s degree in predictive analytics. She excels in R, math, predictive modeling, machine learning, and all things quantitative. She is also excited about applying data science from other domains, to solve interesting workforce optimization challenges.

She applied for a quantitative HR role that promised to let her use her skills and interest in solving difficult employee-based challenges. She was hired for this role. What’s the problem you ask? HR won’t let her do data science.

Over and over again she has suggested a data science approach to help solve employee-focused challenges that have plagued the organization for years, and have cost many millions to the organization’s bottom line. Over and over again she is denied the ability to move forward.

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Her comment is that HR seems to be scared or hesitant in moving forward to a new way of solving solutions. The real concern is that the “reason” was not fully discussed so she could learn.

Instead, she is asked to work on generating monthly or weekly reports that the organization has grown addicted to. When she is allowed to solve an interesting problem using analytics, and brilliantly does so, the executive HR leadership won’t give it executive visibility or implement it in production. Results are found “interesting” but not deployed. Then she’s back to generating reports.

She isn’t alone. And this blog post isn’t about one unique HR data scientist. Not by a long shot. I hear this all the time — thus this post. As a result, I also see brilliant HR data scientists jumping from one company to another. I can see it on LinkedIn updates, and I hear it in the conversations I have with them about why they left and their angst before they leave.

My plea to HR (and to any other department hiring a data scientist)? Stop hiring real data scientists until you’re ready to do real data science.

I think I understand some of the problem. Perhaps the pressure on HR to begin using an analytical approach has led them to hire data scientists, but when it comes to actually using this approach it’s too foreign or scary or “not what we’ve done before.” HR needs to learn from these brilliant people they’re bringing into their domain or stop hiring them to begin with.

In the words of the data scientist I spoke with last week: “Anyone can hire a data scientist. Not every HR department or organization is ready for data science. Generating reports are not analytics — even if they’re prettier or faster reports. Dashboards are not analytics — even if they’re really pretty dashboards. More than anyone, HR should understand the devastating impact of changing job description on someone that’s been hired.”

Ironically, that data scientist hire is perhaps one of the most brilliant and strategic hires that HR department has ever made — perhaps ever. But only if they let her do what she was hired to do. HR data scientists can help move HR from being tactical to strategic, using an analytics approach to highlight never seen before patterns, make decisions based on data, and the like.

Tips on letting that brilliant HR data scientist you hired be one of your most brilliant hires:

1. Assign reporting to someone else. It’s a very important task, but it doesn’t require a data scientist. Reporting will quickly bore them to tears and they’ll resign.

2. Don’t block them from talking directly to your business areas. I often hear they have to go through the HR Business Partner who protects the business leader and blocks them from access. Working with the HR Business Partner of course makes sense. Being blocked by the HR Business Partner doesn’t.

3. Task HR Business Partners with finding either high turnover roles or low performance roles that your data scientist can work to help with.

4. Have them focus first on solving business challenges (like Financial Advisor turnover) not HR challenges like compliance issues. This will give visibility to the great work they do and introduce HR’s new expertise to solving business challenges that affect the bottom line.

5. When they complete an analytics project, give them a chance to talk and present the results, regardless of the outcomes. Did it help or not help? Don’t keep the results inside of HR.

6. Admit that you’re a little nervous about what they do. They’re nervous about what you do too.

7. Trust your data scientist. Stop being scared. You hired them because they have an area of expertise traditional HR doesn’t. Embrace their area of expertise. You need to trust their advice and approach, or, yes, they’ll leave.

And mostly, don’t hire a data scientist if you’re not ready for data science. If you thought you were and you find out later you really aren’t, let them know and let them go. Be honest. Don’t put them in a different role and block them as they keep trying to be successful.

]]>0Stop hiring data scientists until you’re ready for data science80% of consumers have updated their privacy settings, and other barriers to personalizationhttp://venturebeat.com/2015/07/22/80-of-consumers-have-updated-their-privacy-settings-and-other-barriers-to-personalization/
http://venturebeat.com/2015/07/22/80-of-consumers-have-updated-their-privacy-settings-and-other-barriers-to-personalization/#commentsWed, 22 Jul 2015 15:00:08 +0000http://venturebeat.com/?p=1771443VB INSIGHT: Those that wish to reach the utopia of "one-to-one marketing" have a number of hurdles to leap. Fortunately, a new report offers a road map to success.
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VB INSIGHT:

We know that using personalization in marketing works, and we are fast heading toward a future where hyper-personalization will become the norm.

But those who wish to reach the utopia of “one-to-one marketing” have a number of hurdles to leap, one of which is a key finding in my latest State of Marketing Technology report, released today on VB Insight.

It turns out that not only does almost everybody know how to manage and control their privacy settings, almost 80 percent of people have done so.

But privacy settings and limited access to personally identifiable information are not the only barriers for those brands that want to speak to you as if they know you. Or, at the very least, give you the illusion of personal communications.

I discovered that while consumers are happy for you to use their age and gender in personalized advertising, they simply aren’t ready for you to use their own content (such as Instagram pictures) in ads, or interlace publicly available fitness data from wearable devices into ads or offers. Using a home address in an area deemed “public,” (even if it is not, such as a advertisement on Facebook) is also a big no-no.

In fact, I found that 77 percent of “digital natives” — those who have grown up using technology such as the Internet, computers, and mobile devices — fully expect a personalized website experience. Of course, these future customers may want websites to be more personalized, but marketing tactics are no good unless real results are forthcoming.

Fortunately, they are.

Personalized website content increased page views for one company I studied by 300 percent, and another saw an increase in conversion rate of 219 percent.

In total, I surveyed over 1,700 consumers, using data from Survata. I also spoke with and interviewed a number of experts to determine the future of hyper-personalization, and help create a playbook for one-to-one marketing. In the report, I tackle a number of topics, including:

What drives consumers to provide personally identifiable information.

The complexity of the rules, regulations, and laws that come into play when considering one-to-one marketing and personal data.

What consumers are ready to let you use in advertising and what creeps them out.

How big the market for smart beacons is and what they (and location data in general) will mean for the future of retail.

The first milestone on the road to personalized marketing is understanding how consumers feel about it, and this research will help move the train forward for the benefit of both the marketer and the customer.

Founded in 1995, originally as NetStart Inc., CareerBuilder has emerged as one of the most recognizable brands in online recruitment. Its CareerBuilder.com portal is among the largest job sites in the U.S. with 24 million visitors a month, while it also provides human resources software as a service (SaaS) to companies.

Launched out of Amsterdam in 2001, Textkernel’s technology uses machine learning and natural-language processing techniques to extract and make sense of key information from CVs and job descriptions. This helps match job-seekers with vacant positions.

Through acquiring a majority stake in Textkernel, CareerBuilder is looking to bolster its SaaS HR credentials while also helping Textkernel scale its business globally. Indeed, Textkernel’s technology can be integrated into many third-party HR systems, including CareerBuilder’s own pre-hire platform, which launched last month.

Textkernel also has a big-data analytics tool for jobs called Jobfeed, and CareerBuilder will use this to grow its own Supply & Demand service, which assesses how difficult it will be to fill a certain position through measuring the amount of vacancies in that field against the available talent.

CareerBuilder made a similar strategic acquisition last year, when the Chicago-based company snapped up Broadbean, a London-headquartered firm that provides a range of HR-focused software. As with the Textkernel deal, this was designed to expedite CareerBuilder’s shift from a simple job board company into an HR SaaS provider.

More information:

]]>0CareerBuilder acquires Dutch company Textkernel to help bring semantic job searches to recruitersReal data scientists have a rare hybrid of skill sets: Here’s what to look forhttp://venturebeat.com/2015/07/18/real-data-scientists-have-a-rare-hybrid-of-skill-sets-heres-what-to-look-for/
http://venturebeat.com/2015/07/18/real-data-scientists-have-a-rare-hybrid-of-skill-sets-heres-what-to-look-for/#commentsSat, 18 Jul 2015 19:00:00 +0000http://venturebeat.com/?p=1769445GUEST: If you fall prey to these 3 misconceptions about big data and data science, it could cost you.
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GUEST:

Over the course of the last year I’ve spoken with hundreds of employers interested in hiring data scientists, in particular, data scientists with advanced educational degrees. Many employers and hiring managers have heard that big data is the “hot new thing.” But as with all “hot new things,” there’s as much misinformation about data science as there are facts. Here are three misconceptions about big data and data science that I often encounter:

1. Big data is statistics and business intelligence with more data. There’s nothing new here.

This is a view often held by those with limited or no software development experience and it is plainly false. The perfect analogy for this is ice. Ice is just cold water right? There’s nothing new here. However, cooling down water doesn’t just change a quantitative property (temperature) but drastically changes its qualitative properties (transforming a liquid to a solid). The same can be said of more data. Big data strains and ultimately breaks the old paradigms of computation. With big data, all the data cannot fit into RAM and the traditional BI calculations would take years complete. Parallelization and distributed computation are obvious answers to scaling, but this is not always easy: Even a simple statistical tool like logistic regression does not easily parallelize. Distributed statistical computation is as different from traditional business analytics as ice is from water.

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2. Data scientists are just rebranded software engineers.

Sometimes engineers with strong software development backgrounds will rebrand as data scientists for the salary premium. This can lead to subpar results. At the simplest level, debugging stats bugs becomes much harder. Engineers are trained to spot and solve programming bugs. But without a solid background in probability and statistics, they often have a hard time solving statistical bugs. Your code might be just fine but if you didn’t reweight your training examples correctly, your predictions will be off.

At a higher level, engineers are well trained to build simple discrete rules-based models. But these models are ill-suited to derive the more subtle insights from continuous-valued data and are leaving money on the table. Solid statistical chops are necessary to overcome these challenges to build the next generation of scalable predictive models.

3. Data scientists don’t need to understand the business, the data will tell you everything.

People with machine-learning backgrounds often succumb to this one, in part because machine learning is so powerful. But it is not omnipotent. Searching for all possible correlations is time consuming, not to mention statistically problematic. Data scientists need to be guided by business intuition to help them distinguish between spurious correlations and real ones. Lack of domain expertise can lead to ill-founded conclusions (“more police officers leads to higher crime rates”) that prompt bad policy recommendations (“cut the policing staff in high crime neighborhoods”). Finally, having business intuition is also important for convincing key stakeholders. These stakeholders might not be data-scientists but are usually domain experts: Talking about your correlations in a language they can understand is key to getting the kind of institutional buy-in that is necessary for data science to achieve its promise.

Big data and data science is about building the right model that combines the right engineering, statistical, and business skills. Without all three, your data scientists will not be able to achieve everything they set out to do.

Michael Li is founder and executive director of data science fellowship program The Data Incubator. He was formerly a data science lead at both Foursquare and Andreessen Horowitz and spent time as a NASA researcher and Wall Street quant. You can follow him on Twitter @tianhuil.

]]>0Real data scientists have a rare hybrid of skill sets: Here’s what to look forIntent data: Does it live up to the hype?http://venturebeat.com/2015/07/16/intent-data-does-it-live-up-to-the-hype/
http://venturebeat.com/2015/07/16/intent-data-does-it-live-up-to-the-hype/#commentsFri, 17 Jul 2015 01:00:51 +0000http://venturebeat.com/?p=1768710GUEST: With all the talk about predictive-driven sales and marketing, a new question is emerging – which data is most valuable?
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GUEST:

With all the talk about predictive-driven sales and marketing, a new question is emerging – which data is most valuable? Many B2B businesses are achieving unprecedented customer insight by leveraging all kinds of external demographic and firmographic data to see if a company is a good fit for their product. Some are pairing that with signals from their marketing automation systems and web analytics to predict whether a prospect might be ready to buy soon.

Now, another breed of “intent data” has emerged. External data providers like Bombora, The Big Willow, IDG and TechTarget are aggregating information about web visitors on B2B publisher networks to help businesses figure out when certain prospects might be in the market for their product. This kind of insight presents an exciting new frontier for data-driven marketing.

Intent Data Defined

As with any new data source, it’s helpful to have a clear understanding of what it includes and how it can be applied. Intent data generally falls in one of two main categories, which each best serves a different purpose:

Internal Intent Data (also referred to as first-party data) is the activity a company captures on its own website or through application logs. This kind of information usually contains highly predictive buying signals because the content is so relevant to the purchase decision — i.e. exactly what pages a prospect touched, which links they clicked on, and how long they spent on each page.

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External Intent Data (also referred to as third-party data) is collected by publisher networks either at the IP level, or through user registration and shared cookies. These sites track the articles a user reads, content they download, their site searches, and potentially even comments they leave. For example, data might show that people from the ibm.com domain are viewing more articles than normal about “help desk software,” which could provide a hint about IBM’s software needs.

Sound a bit creepy? To some people, it is. There’s a reason that Google doesn’t open up its search database and LinkedIn won’t sell its social graph — that kind of personal data is super sensitive and core to their businesses. But many publishers are pushing the boundaries of privacy and finding new ways to monetize their traffic. Several have loosened their terms of service to gain the leeway to track individual actions and tell outsiders what an IP address or even a registered user is doing. This is something consumers should keep in mind if they don’t want their behavior to be monitored across the web.

Use Cases: Intent Data in Action

Although it’s early days for third-party intent data, it seems obvious that the behavior bread crumbs people leave across the web must be valuable for marketers, especially if they indicate when a prospect’s interest is surging. But how do you turn this insight into impact? Some companies use intent data to generate lists of net-new leads that seem interested in a specific product category. This lets sales development reps target folks who are warmer than a cold list.

Another use case we’ve seen is leveraging intent data to glean insight into the existing prospects in a CRM database. If you find new clues about these accounts, you can potentially append behavioral score categories to applicable records. As a result, companies might be able to better prioritize sales outreach or personalize messages in order to boost click-throughs — for example by finding companies that are reading lots of articles about cloud computing and sending them a relevant email campaign on that subject.

Pitfalls to Avoid

As with any new type of data, there are always shortcomings to consider, which vary a bit for each of the above-mentioned use cases. Our company regularly tests new data sources, and we’ve taken a close look at third-party intent data to evaluate the benefits it can deliver for marketing.

For net-new leads, it’s important to consider that most external intent data is aggregated at the domain level. Reps won’t know who read the articles, and will have to spend time identifying the right contacts at each account in order to effectively prospect these leads. They’ll often have cold conversations and end up on wild goose chases, because a spike in articles consumed by a domain doesn’t necessarily mean that the relevant contact is interested or that the company is actively evaluating and ready to buy.

In the second use case, a business wants to know more about prospects that are already in its database but might not be actively engaging on the web site. Questions to think about here are coverage and accuracy. What percentage of sales records have a match in the most recent intent database? When we ran our tests, 86 percent of the companies in our sample set had no third-party intent data associated with them. Companies should also look for activity around the subjects that are relevant to them (e.g. “people reading articles about help desk software”). However, when we filtered down to even the most popular topics, match rates often dropped below 2.4 percent. Generally speaking, when evaluating data, the goal is to find insight that impacts a larger percentage of the pipeline so it can drive a material impact.

The next question to ask when a signal is present is whether it’s accurate. Is a surging account highly correlated with a prospect being in-market? We had an intent data vendor match records for a diverse set of our customers, and then ran historical backtesting. What we found was that the intent flag was no better at predicting opportunities than random chance. We’ll continue to test intent, but in the meantime we recommend focusing on use cases where the risk of being wrong is low. For example, marketing teams might use third-party intent data to personalize emails and advertising campaigns, which could increase click-through rates (a great way to generate more first-party intent data).

Keys to Success: Making the Most of Intent Data

If you’re considering external intent data, the most common mistake you can make is to jump in without defining clear use cases or your ideal result. While everyone knows intuitively that there are prospects they aren’t seeing and would like more insight, if you want to drive real impact, it is important to start with your criteria for measuring success and work backwards. For example, with the net-new use case, we’re optimistic that there could be value in intent data — it’s just a question of how many leads you can generate, how much sales effort it takes, and ultimately whether it’s worth the cost per good lead. That type of framework provides an objective way to compare intent data with other lead sources or list buys.

Vik Singh is a cofounder and chief executive of Infer. You can follow him at @zooie.

Jamie Grenney is vice president of marketing at Infer. You can follow him at @jamiegrenney.

Last quarter investors threw almost $700 million at marketers’ biggest problem: analytics. Today, Origami Logic presented its latest solution to the flood of data that marketing technology platforms generate, consume, and present.

The goal? Making sense of big data chaos.

Today Origami launched what it calls a “Marketing Signals Framework,” which aggregates, organizes, and prioritizes the data that marketers need to review to understand if they’re winning. Those marketing signals are from ad campaigns, search, mobile apps, email campaigns, web platforms, even print and TV marketing.

“Marketing has become a complex, rapidly changing, noisy daily battle,” said Opher Kahane, cofounder and CEO of Origami Logic. “Cutting through this noise and measuring the signals that matter has become a strategic priority.”

Making high-level sense of marketing data is an evolving category at the top end of the exploding analytics space that includes newcomers such as Domo, Beckon, and Origami, as well as more traditional business intelligence tools. They address the challenge that, as Beckon CEO Jenny Zeszut told me a year ago, CMOs have more data than ever before, and are using it less and less … simply because it’s difficult to make sense of the firehose of non-prioritized data.

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Origami launched a year ago to solve the problem, and today’s update aims to unify a view of marketing progress via signals that are categorized into larger objectives, channels, and kind.

Here are a few examples:

Above: Example marketing signals in Origami

Image Credit: John Koetsier

Some of these signals represent the end of a specific campaign, Kahane told me via email. But many reflect the ongoing flow of modern marketing in which marketing technologies “continuously adjust and optimize campaigns mid-flight.”

The signals are also ranked, organized, and color-coded by prospect/customer level: from awareness to engagement, from engagement to conversion, from conversion to loyalty, and from loyalty to advocacy.

(I note Origamic doesn’t have another level — hatred and loathing — that some customers of certain companies seem to attain.)

So it’s not just all about ROI:

“Signals represent results of marketing activity, and ROI is just one example,” Kahane said. “However, signals go beyond representing results. Signals serve as the bridge between raw data and insights, allowing marketers to gain immediate and clear direction on where opportunities lie and how to further improve results.”

Part of the challenge for modern marketers who are confronted with a ceaseless flow of data from an overwhelming variety of channels is knowing if they’re being successful. If they’re winning, in other words. Fixating on one or two metrics is one possible result, with the challenge that those may not be the right ones.

“Keeping score is important, but what is more important is to find ways to win,” said Kahane. “Knowing that you are down 1-3 is useless if it does not help you figure out a way to turn the game around. The Marketing Signals Framework provides a way to not only understand what your score is right now, but also to distinguish ‘real’ goals from ‘vanity’ goals, find ways to score goals faster and better, and make sure that the goals you are scoring are adding up to your objective.”

Beckon launched omnichannel scorecards early this year. Domo brings dashboards from over 300 marketing systems into one dashboard to rule them all. It’ll be interesting to see how marketers react to Origami’s classification and categorization of all marketing signals.

Personalization is one of the most often-heard buzzwords in marketing today — yet get a group of marketers together, and you’ll likely hear as many definitions of the term as there are people in the room. And while it may mean different things to different people, here’s the thing: by its very nature, personalization is an ever-evolving notion. It’s about creating a dynamic environment customized and optimized for every user in order to extract the maximum value from each engagement with them — and extract maximum ROI.

Getting there requires an investment — organizationally and technologically. Marketers need a single view of the customer that enables them to deliver personalized experiences. They need to know how to align their technology and fragmented, silo-based organizational structure to start building that single view of the customer to increase lifetime-value.

And to get there they need a roadmap.

The personalization maturity curve

In an interview with VB, Sailthru’s EVP of Customer Success, Cassie Lancellotti-Young, explained how Sailthru helps clients “move up in their personalization maturity” to a point where they’re comfortable and understand what’s needed to achieve fully-automated, fully-personalized, one-to-one communications at scale.

While every organization is different, all companies can take incremental steps towards the final goal of one-to-one personalization following this maturity curve.

Luckily, it’s pretty straightforward:

Phase 1: Basic batch-and-blast approach — In this first phase, marketers simply stick to single message mailing, like email, where everyone gets the exact same thing. This is still standard operating procedure for many brands, and while it can produce a positive ROI, that return is baseline at best.

Phase 2: Field insertion — In this next phase, companies insert something personal, often the first name, the city a customer lives in, or gender-specific information into marketing messages. Lancellotti-Young cites American jewelry retailer, Alex and Ani, as a successful case study in this early stage of personalization maturity: When they used field insertion alone it increased their open rates by 6 percent.

Phase 3: Traditional segmentation or rules-based messaging — Continuing on, marketers segment their customer base using demographic data such as location, then send marketing messaging relevant to the segment. According to Lancellotti-Young, Alex and Ani used geographical segmentation for a campaign on bangles for local baseball teams. Customers who lived in San Francisco got messaging about bangles for the Giants while New Yorkers got messaging about Yankees bangles. Segmentation increased Alex and Ani’s click rates by 127 percent. Note that this is the stage of personalization maturity where many marketers find themselves stuck, often held back by limitations in their marketing technology.

Phase 4: Behavioral recommendations — Looking beyond what you know explicitly about your customer, this next phase gets into what you know implicitly about them. What do they look at on the website? What are their interests? Could they live in San Francisco but be, in fact, Yankees fans, so they consume Yankees content more? Lancellotti-Young tells VB about Country Outfitter: they used behavioral recommendations to achieve 109 percent lift in email conversion. “With every incremental step taken on the [personalization maturity] curve,” she says, “you get paid dividends in terms of ROI numbers on the metrics that really matter.”

Phase 5: Omnichannel optimized — Every channel is integrated and talking to one another in this phase. Marketing communications are optimized across all channels, not only pulling in data from all these channels, but also actively pushing it out to them based on that single customer view. At this stage marketers can expect an average of 20 percent lift in Customer Lifetime Value (CLV), according to Sailthru’s data.

Phase 6: Predictive intelligence / personalization — Not just looking at data from the past, but using that data to determine what customers are likely to do in the future is the holy grail of the personalization maturity curve. At this stage, predictive intelligence drives personal customer experiences at scale and success at this stage fundamentally changes companies. Think of this as predictive personalization: it enables brands to automatically optimize messaging based on a customers’ future behavior. When you can anticipate what kind of communication a customer will respond to, if they’ll purchase, how much they’ll spend — or if they’re at risk of opting-out — you can personalize content, messaging, calls-to-action and even discounts in a way that optimizes revenue with every engagement.

The height of personalization maturity

Organizations that don’t have a single view of their customers, and are unable to provide personalized experiences to them, interact like they don’t know them at all. On the other hand, fully mature personalization enables companies to move beyond treating customers like strangers and increases not only customer lifetime value, but the customer lifetime experience and, therefore, their loyalty.

In fact, many organizations stuck further down the curve will lose CLV by focusing more on acquisition than loyalty, as batch-and-blast marketing does. Do you tend to capture lead data like emails through contests and sweepstakes? It’s a quick acquisition strategy and it brings a lot of people through the door quickly. But these leads are 63 percent less likely to convert and 53 percent more likely to opt out of email when compared to leads obtained by other means of acquisition such as paid channels.

This is the essence of the personalization maturity curve: deliver great personalized experiences to your customers and you increase their brand loyalty. You don’t even have to capture leads based on discounting. They’ll willingly convert at full value if you enrich their experiences.

The foundations of omnichannel optimization and one-to-one personalization

To be operating toe-to-toe with today’s consumers requires change. But transformation doesn’t happen in a vacuum — it takes great people, great ideas, and great technology. The greatest marketers of our time are quickly moving up the personalization maturity curve by solving these key challenges:

Breaking down organization silos: When sales, marketing, and customer success/support teams are all acting independently, it is nearly impossible to achieve true personalization. This business-as-usual structure fails to encourage a single view of the customer, and results in a fractured customer experience that will leave you stuck in the first stages of the personalization maturity curve.

Ensuring customer data collection: You can’t personalize marketing efforts without knowing your customer: their behavior in each channel, on every device, and at every touch point with your company and how that persists and changes over time.

Turning data into intelligence: Data on its own is meaningless. Extracting insights using the right technology and analytic platforms is key to personalization.

Connecting the customer experience: By understanding who the customer is, and their individual preferences, brands can anticipate customer needs. This is what makes for superb customer experiences that keep pace with customers every step of the way: from acquisition, to purchase, to advocacy, to repurchase — over and over.

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]]>0The personalization curve: Just how far along is your brand?Microsoft buys field-service management software maker FieldOnehttp://venturebeat.com/2015/07/16/microsoft-buys-field-service-management-software-maker-fieldone/
http://venturebeat.com/2015/07/16/microsoft-buys-field-service-management-software-maker-fieldone/#commentsThu, 16 Jul 2015 13:00:07 +0000http://venturebeat.com/?p=1768504Microsoft today announced that it has acquired FieldOne, a company with software for managing the activity of field workers. Terms of the deal weren’t disclosed. The deal comes four months after the two companies announced a “multi-year agreement” that would allow companies to access Microsoft Dynamics and FieldOne’s tools in one package. Buying FieldOne instantly […]
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Microsoft today announced that it has acquired FieldOne, a company with software for managing the activity of field workers. Terms of the deal weren’t disclosed.

The deal comes four months after the two companies announced a “multi-year agreement” that would allow companies to access Microsoft Dynamics and FieldOne’s tools in one package.

Buying FieldOne instantly turns Microsoft into a go-to provider for yet another type of business software and helps the tech giant diversify itself a little bit further. But perhaps more importantly, over time Microsoft can set a new standard for what field service software can be, by connecting it with sophisticated tools like the Cortana personal digital assistant that’s part of the recently announced Cortana Analytics Suite.

“Field service businesses are aggressively trying to move away from a reactive break-fix model to predictive service based model,” Bob Stutz, corporate vice president of Microsoft Dynamics CRM, wrote in a blog post on today’s news. “With this acquisition, Microsoft can help companies tap the potential of predictive service by bringing together the powerful combination of FieldOne, Azure IoT and Cortana Analytics. That means that organizations can use insights that effectively provide servicing proactively while streamlining the provisioning of service to significantly reduce costs.”

FieldOne started in 2001 and is based in Mahwah, N.J. Customers include Climatec, Mitsubishi-Hitachi Power Systems, and Carl Zeiss. The company’s software can run in companies’ on-premises data centers or as a cloud service. Mobile apps are available for field service workers.

Other vendors in the field service management business include Jobber, mHelpDesk, ServicePower, and Wintac.

In a match that rivaled anything The Bachelor has ever offered, the phone company with massive data on consumers wed AOL with its growing ad delivery networks.

Today, startup Cinarra Systems is announcing it has landed $20 million to make consumer data from non-Verizon mobile carriers available for segmented targeting by advertisers.

In other words, CEO Alex Zinin told me, his company is like an open version of AOL that is “connecting the two industries.”

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To underscore the importance of that union, the new Series B round was led by the Japan-based telecom subsidiary of conglomerate SoftBank Group. Cinarra has established a branch in Japan specifically for that market.

Founded in 2012, the Santa Clara, Calif-based company said it expects to launch its platform before summer’s end. In real time, this “carrier-oriented DSP” (demand-side platform) buys space for ads, and then, using mobile carrier data, delivers those ads.

“The carriers are sitting on a golden pile of data,” he told me, adding that the key question is “how to monetize it without violating [user] privacy.”

That means, for instance, that an ad for monogrammed coffee mugs targeted and delivered through Cinarra might be shown on the CNN mobile website for a group of anonymous users who are near a Starbucks. It wouldn’t be targeted just to Jane Smith, coffee drinker.

He assured me the data is anonymized, will never be made directly available to the advertisers, and will never be used for individual targeting.

Zinin added that SoftBank sees this as the beginning of a new phase, when telco operators connect their data to Net platforms like online publishers and digital advertisers do. Gartner has predicted the global mobile ad market will balloon to $42 billion by 2017, four times what it was in 2012.

I asked him why telcos, which have had data on the calls we make, where we live, what services we use and so on for nearly forever, are just now getting around to using it.

After all, didn’t Verizon realize it was sitting on a goldmine of data?

Zinin replied that, while he couldn’t speak for Verizon, telcos in general have been focused over the last several years on building higher speed networks. While ad targeting to smartphones and tablets has been going through their systems, they’ve been on the sidelines.

They “were out of the game,” he said, but now they understand they’re not just telecoms “but data warehouses.”

There are many DSPs out there serving targeted ads, he acknowledged, but he contended that Cinarra is “creating a new kind of data, a new kind of campaign.”

“Most of the mobile app ads,” he noted, “are about other apps or [they are] garbage.” He added that the ads Cinarra’s platform can deliver to mobile web browsers and apps will be more relevant to the targeted group of people.

In contrast to those other DSPs, he said, Cinarra’s access to carrier data means a greater scale of data, a greater access to location-based information, and, because you’re talking about a service-level view of customers rather than an app- or browser-level view, potentially different kinds of targeting data.

In addition to more relevant ads on your smartphone, Zinin suggested that this new revenue stream will allow telcos to more readily subsidize new services to consumers.

But we’ll see about that part.

The new Series B funding, which brings the total raised by Cinarra to about $24.7 million, will be used to scale the engineering and sales teams. Besides SoftBank, other investors in this round were Almaz Capital and Siguler Guff & Company.